{ "cells": [ { "cell_type": "markdown", "id": "894c3e8d-5ece-4216-9b5f-ceb9d61bc0b8", "metadata": {}, "source": [ "**Part-A**" ] }, { "cell_type": "code", "execution_count": 16, "id": "f6be3896-2658-4cc0-8b68-b8f4dd883942", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import os\n", "import re\n", "import pickle\n", "import scipy.stats as stats\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matplotlib inline\n", "import plotly.express as px\n", "import plotly.graph_objects as go\n", "from plotly.subplots import make_subplots\n", "from matplotlib.pyplot import pie, axis, show" ] }, { "cell_type": "code", "execution_count": 17, "id": "c35558e8-b7a8-49ed-b03f-d8baf36a20e6", "metadata": {}, "outputs": [], "source": [ "import missingno as msno" ] }, { "cell_type": "code", "execution_count": 18, "id": "89ccc7af-d40a-444b-b608-e63d47cfa74a", "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import LabelEncoder\n", "from sklearn.preprocessing import MinMaxScaler\n", "from scipy.stats import zscore\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn import neighbors, datasets, preprocessing\n", "from sklearn.metrics import accuracy_score, confusion_matrix\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.svm import SVC\n", "from sklearn.metrics import classification_report\n", "from sklearn.preprocessing import StandardScaler, LabelEncoder\n", "from sklearn import model_selection\n", "from collections import Counter\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.metrics import accuracy_score, precision_score, recall_score, roc_auc_score, f1_score, r2_score\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "from sklearn import neighbors, datasets, preprocessing\n", "from sklearn.model_selection import train_test_split\n", "from imblearn.over_sampling import SMOTE\n", "from sklearn.ensemble import BaggingClassifier\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier, GradientBoostingClassifier, VotingClassifier, StackingClassifier\n", "from xgboost import XGBClassifier, XGBRegressor\n", "from lightgbm import LGBMClassifier\n", "from catboost import CatBoostClassifier" ] }, { "cell_type": "code", "execution_count": 19, "id": "d1f9a187-7d91-4a65-984c-0595ba83d5b3", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "from imblearn.over_sampling import SMOTE\n", "from sklearn.ensemble import BaggingClassifier\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier, GradientBoostingClassifier, VotingClassifier, StackingClassifier\n", "from xgboost import XGBClassifier, XGBRegressor\n", "from lightgbm import LGBMClassifier\n", "from catboost import CatBoostClassifier\n", "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, roc_auc_score, f1_score, ConfusionMatrixDisplay, r2_score" ] }, { "cell_type": "code", "execution_count": 20, "id": "ae84e714-5d5e-4cb0-a325-2a1ad5f7259e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "[(1.0, 0.34509803921568627, 0.3176470588235294),\n", " (0.9529411764705882, 0.7568627450980392, 0.18823529411764706),\n", " (0.2549019607843137, 0.2901960784313726, 0.4196078431372549),\n", " (0.7058823529411765, 0.6039215686274509, 0.5215686274509804),\n", " (0.10980392156862745, 0.10588235294117647, 0.12549019607843137)]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from imblearn.over_sampling import SMOTE\n", "from sklearn.ensemble import BaggingClassifier\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier, GradientBoostingClassifier, VotingClassifier, StackingClassifier\n", "from xgboost import XGBClassifier, XGBRegressor\n", "from lightgbm import LGBMClassifier\n", "from catboost import CatBoostClassifier\n", "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, roc_auc_score, f1_score, ConfusionMatrixDisplay, r2_score\n", "np.random.seed(42)\n", "from sklearn.preprocessing import StandardScaler\n", "from imblearn.over_sampling import SMOTE\n", "from sklearn.ensemble import BaggingClassifier\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier, GradientBoostingClassifier, VotingClassifier, StackingClassifier\n", "from xgboost import XGBClassifier, XGBRegressor\n", "from lightgbm import LGBMClassifier\n", "from catboost import CatBoostClassifier\n", "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, roc_auc_score, f1_score, ConfusionMatrixDisplay, r2_score\n", "\n", "colors = ['#FF5851', '#F3C130', '#414A6B', '#B49A85', '#1C1B20']\n", "sns.color_palette(colors)" ] }, { "cell_type": "markdown", "id": "7b26ae68-456f-4d2a-b640-c4eee6724342", "metadata": {}, "source": [ "**DOMAIN:** *Telecom*\n", "\n", "\n", "**CONTEXT:** *A telecom company wants to use their historical customer data to predict behaviour to retain customers. You can analyse all\n", "relevant customer data and develop focused customer retention programs.*\n", "\n", "\n", "**DATA DESCRIPTION:** *Each row represents a customer, each column contains customer’s attributes described on the column Metadata. The\n", "data set includes information about:*\n", "\n", "\n", "*Customers who left within the last month – the column is called Churn*\n", "\n", "\n", "*Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and\n", "streaming TV and movies*\n", "\n", "\n", "*Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges*\n", "\n", "\n", "*Demographic info about customers – gender, age range, and if they have partners and dependents*\n", "\n", "\n", "**PROJECT OBJECTIVE:** *To Build a model that will help to identify the potential customers who have a higher probability to churn. This helps the\n", "company to understand the pinpoints and patterns of customer churn and will increase the focus on strategizing customer retention*" ] }, { "cell_type": "markdown", "id": "abed0e0d-07d8-4662-99c9-961f286cecab", "metadata": {}, "source": [ "**1. Data Understanding & Exploration:**" ] }, { "cell_type": "markdown", "id": "a5caba1e-aa1e-40a0-9f55-b929401e2fc4", "metadata": {}, "source": [ "**A. Read ‘TelcomCustomer-Churn_1.csv’ as a DataFrame and assign it to a variable.**" ] }, { "cell_type": "code", "execution_count": 21, "id": "9dd6f2c0-ce00-4940-9fc1-ee0f9472f397", "metadata": {}, "outputs": [], "source": [ "df1 = pd.read_csv('TelcomCustomer-Churn_1.csv')" ] }, { "cell_type": "code", "execution_count": 22, "id": "44e41031-5247-4526-9fb8-bc7fc09e9725", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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customerIDgenderSeniorCitizenPartnerDependentstenurePhoneServiceMultipleLinesInternetServiceOnlineSecurity
07590-VHVEGFemale0YesNo1NoNo phone serviceDSLNo
15575-GNVDEMale0NoNo34YesNoDSLYes
23668-QPYBKMale0NoNo2YesNoDSLYes
37795-CFOCWMale0NoNo45NoNo phone serviceDSLYes
49237-HQITUFemale0NoNo2YesNoFiber opticNo
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" ], "text/plain": [ " customerID gender SeniorCitizen Partner Dependents tenure PhoneService \\\n", "0 7590-VHVEG Female 0 Yes No 1 No \n", "1 5575-GNVDE Male 0 No No 34 Yes \n", "2 3668-QPYBK Male 0 No No 2 Yes \n", "3 7795-CFOCW Male 0 No No 45 No \n", "4 9237-HQITU Female 0 No No 2 Yes \n", "\n", " MultipleLines InternetService OnlineSecurity \n", "0 No phone service DSL No \n", "1 No DSL Yes \n", "2 No DSL Yes \n", "3 No phone service DSL Yes \n", "4 No Fiber optic No " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.head()" ] }, { "cell_type": "code", "execution_count": 23, "id": "f08ba235-e9a6-4c47-994b-57f4b4fd911c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(7043, 10)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.shape" ] }, { "cell_type": "markdown", "id": "7bd34665-17e3-4730-a7d6-21c80f6f6708", "metadata": {}, "source": [ "**B. Read ‘TelcomCustomer-Churn_2.csv’ as a DataFrame and assign it to a variable.**" ] }, { "cell_type": "code", "execution_count": 24, "id": "e269737a-2fd4-41ef-96af-c36b0ebc9e6d", "metadata": {}, "outputs": [], "source": [ "df2 = pd.read_csv('TelcomCustomer-Churn_2.csv')" ] }, { "cell_type": "code", "execution_count": 25, "id": "c1b7050f-5a86-41fa-bb08-fdd4e764a1ea", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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customerIDOnlineBackupDeviceProtectionTechSupportStreamingTVStreamingMoviesContractPaperlessBillingPaymentMethodMonthlyChargesTotalChargesChurn
07590-VHVEGYesNoNoNoNoMonth-to-monthYesElectronic check29.8529.85No
15575-GNVDENoYesNoNoNoOne yearNoMailed check56.951889.5No
23668-QPYBKYesNoNoNoNoMonth-to-monthYesMailed check53.85108.15Yes
37795-CFOCWNoYesYesNoNoOne yearNoBank transfer (automatic)42.301840.75No
49237-HQITUNoNoNoNoNoMonth-to-monthYesElectronic check70.70151.65Yes
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" ], "text/plain": [ " customerID OnlineBackup DeviceProtection TechSupport StreamingTV \\\n", "0 7590-VHVEG Yes No No No \n", "1 5575-GNVDE No Yes No No \n", "2 3668-QPYBK Yes No No No \n", "3 7795-CFOCW No Yes Yes No \n", "4 9237-HQITU No No No No \n", "\n", " StreamingMovies Contract PaperlessBilling PaymentMethod \\\n", "0 No Month-to-month Yes Electronic check \n", "1 No One year No Mailed check \n", "2 No Month-to-month Yes Mailed check \n", "3 No One year No Bank transfer (automatic) \n", "4 No Month-to-month Yes Electronic check \n", "\n", " MonthlyCharges TotalCharges Churn \n", "0 29.85 29.85 No \n", "1 56.95 1889.5 No \n", "2 53.85 108.15 Yes \n", "3 42.30 1840.75 No \n", "4 70.70 151.65 Yes " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.head()" ] }, { "cell_type": "code", "execution_count": 26, "id": "8d679547-1028-49a1-ae59-1cdc511eae00", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(7043, 12)" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.shape" ] }, { "cell_type": "markdown", "id": "c2558a71-2a7a-43af-8d2a-0e2fcc34c7a1", "metadata": {}, "source": [ "**C. Merge both the DataFrames on key ‘customerID’ to form a single DataFrame**" ] }, { "cell_type": "code", "execution_count": 27, "id": "2c3d93c3-2664-4095-ae87-af0ca07d8c7a", "metadata": {}, "outputs": [], "source": [ "df=pd.merge(df1, df2, on='customerID')" ] }, { "cell_type": "code", "execution_count": 28, "id": "9e6d9fc7-8069-44eb-b078-dcea4f374c7d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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customerIDgenderSeniorCitizenPartnerDependentstenurePhoneServiceMultipleLinesInternetServiceOnlineSecurity...DeviceProtectionTechSupportStreamingTVStreamingMoviesContractPaperlessBillingPaymentMethodMonthlyChargesTotalChargesChurn
07590-VHVEGFemale0YesNo1NoNo phone serviceDSLNo...NoNoNoNoMonth-to-monthYesElectronic check29.8529.85No
15575-GNVDEMale0NoNo34YesNoDSLYes...YesNoNoNoOne yearNoMailed check56.951889.5No
23668-QPYBKMale0NoNo2YesNoDSLYes...NoNoNoNoMonth-to-monthYesMailed check53.85108.15Yes
37795-CFOCWMale0NoNo45NoNo phone serviceDSLYes...YesYesNoNoOne yearNoBank transfer (automatic)42.301840.75No
49237-HQITUFemale0NoNo2YesNoFiber opticNo...NoNoNoNoMonth-to-monthYesElectronic check70.70151.65Yes
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5 rows × 21 columns

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" ], "text/plain": [ " customerID gender SeniorCitizen Partner Dependents tenure PhoneService \\\n", "0 7590-VHVEG Female 0 Yes No 1 No \n", "1 5575-GNVDE Male 0 No No 34 Yes \n", "2 3668-QPYBK Male 0 No No 2 Yes \n", "3 7795-CFOCW Male 0 No No 45 No \n", "4 9237-HQITU Female 0 No No 2 Yes \n", "\n", " MultipleLines InternetService OnlineSecurity ... DeviceProtection \\\n", "0 No phone service DSL No ... No \n", "1 No DSL Yes ... Yes \n", "2 No DSL Yes ... No \n", "3 No phone service DSL Yes ... Yes \n", "4 No Fiber optic No ... No \n", "\n", " TechSupport StreamingTV StreamingMovies Contract PaperlessBilling \\\n", "0 No No No Month-to-month Yes \n", "1 No No No One year No \n", "2 No No No Month-to-month Yes \n", "3 Yes No No One year No \n", "4 No No No Month-to-month Yes \n", "\n", " PaymentMethod MonthlyCharges TotalCharges Churn \n", "0 Electronic check 29.85 29.85 No \n", "1 Mailed check 56.95 1889.5 No \n", "2 Mailed check 53.85 108.15 Yes \n", "3 Bank transfer (automatic) 42.30 1840.75 No \n", "4 Electronic check 70.70 151.65 Yes \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 29, "id": "21173feb-087f-4a06-af7c-eb63586b6d52", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 7043 entries, 0 to 7042\n", "Data columns (total 21 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 customerID 7043 non-null object \n", " 1 gender 7043 non-null object \n", " 2 SeniorCitizen 7043 non-null int64 \n", " 3 Partner 7043 non-null object \n", " 4 Dependents 7043 non-null object \n", " 5 tenure 7043 non-null int64 \n", " 6 PhoneService 7043 non-null object \n", " 7 MultipleLines 7043 non-null object \n", " 8 InternetService 7043 non-null object \n", " 9 OnlineSecurity 7043 non-null object \n", " 10 OnlineBackup 7043 non-null object \n", " 11 DeviceProtection 7043 non-null object \n", " 12 TechSupport 7043 non-null object \n", " 13 StreamingTV 7043 non-null object \n", " 14 StreamingMovies 7043 non-null object \n", " 15 Contract 7043 non-null object \n", " 16 PaperlessBilling 7043 non-null object \n", " 17 PaymentMethod 7043 non-null object \n", " 18 MonthlyCharges 7043 non-null float64\n", " 19 TotalCharges 7043 non-null object \n", " 20 Churn 7043 non-null object \n", "dtypes: float64(1), int64(2), object(18)\n", "memory usage: 1.2+ MB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 30, "id": "d0166395-da3f-46cd-ac27-aa95aac4a578", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(7043, 21)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 31, "id": "fb2274e1-3022-447a-bb1d-4e3f1ec10139", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',\n", " 'tenure', 'PhoneService', 'MultipleLines', 'InternetService',\n", " 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',\n", " 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',\n", " 'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'],\n", " dtype='object')" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 32, "id": "8f9cec41-5743-4624-a5a2-fdd136380f05", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',\n", " 'tenure', 'PhoneService', 'MultipleLines', 'InternetService',\n", " 'OnlineSecurity'],\n", " dtype='object')" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df1.columns" ] }, { "cell_type": "code", "execution_count": 33, "id": "769a7a41-4169-4079-8199-e89dd1a4208e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['customerID', 'OnlineBackup', 'DeviceProtection', 'TechSupport',\n", " 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',\n", " 'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'],\n", " dtype='object')" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.columns" ] }, { "cell_type": "markdown", "id": "81b24aad-602c-4b63-9466-be551920ae06", "metadata": {}, "source": [ "**D. Verify if all the columns are incorporated in the merged DataFrame by using simple comparison Operator in Python**" ] }, { "cell_type": "code", "execution_count": 34, "id": "c1fe644d-a6a7-4646-abfe-c23bd1ff5eaf", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Columns are identical.'" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "columns1=set(df1.columns.to_list().__add__(df2.columns.to_list()))\n", "columns2=set(df.columns.to_list())\n", "'Columns are identical.' if columns1==columns2 else 'Columns are not identical.'" ] }, { "cell_type": "markdown", "id": "b06a4cbb-2377-44cb-9ac9-39952c714b3f", "metadata": {}, "source": [ "**2. Data Cleaning & Analysis:**" ] }, { "cell_type": "markdown", "id": "e1686080-debc-4796-b8c0-95aa84133bae", "metadata": {}, "source": [ "**A. Impute missing/unexpected values in the DataFrame.**" ] }, { "cell_type": "code", "execution_count": 35, "id": "a26d704c-5d5d-4797-8f39-00c094cf1d42", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 7043 entries, 0 to 7042\n", "Data columns (total 21 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 customerID 7043 non-null object \n", " 1 gender 7043 non-null object \n", " 2 SeniorCitizen 7043 non-null int64 \n", " 3 Partner 7043 non-null object \n", " 4 Dependents 7043 non-null object \n", " 5 tenure 7043 non-null int64 \n", " 6 PhoneService 7043 non-null object \n", " 7 MultipleLines 7043 non-null object \n", " 8 InternetService 7043 non-null object \n", " 9 OnlineSecurity 7043 non-null object \n", " 10 OnlineBackup 7043 non-null object \n", " 11 DeviceProtection 7043 non-null object \n", " 12 TechSupport 7043 non-null object \n", " 13 StreamingTV 7043 non-null object \n", " 14 StreamingMovies 7043 non-null object \n", " 15 Contract 7043 non-null object \n", " 16 PaperlessBilling 7043 non-null object \n", " 17 PaymentMethod 7043 non-null object \n", " 18 MonthlyCharges 7043 non-null float64\n", " 19 TotalCharges 7043 non-null object \n", " 20 Churn 7043 non-null object \n", "dtypes: float64(1), int64(2), object(18)\n", "memory usage: 1.2+ MB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 36, "id": "81eeb231-b12f-4ca3-bdf9-514f7e2ce94e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "customerID object\n", "gender object\n", "SeniorCitizen int64\n", "Partner object\n", "Dependents object\n", "tenure int64\n", "PhoneService object\n", "MultipleLines object\n", "InternetService object\n", "OnlineSecurity object\n", "OnlineBackup object\n", "DeviceProtection object\n", "TechSupport object\n", "StreamingTV object\n", "StreamingMovies object\n", "Contract object\n", "PaperlessBilling object\n", "PaymentMethod object\n", "MonthlyCharges float64\n", "TotalCharges object\n", "Churn object\n", "dtype: object" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": 37, "id": "d6798040-ff82-4dec-9bb7-acb8ea1816f7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "customerID 0\n", "gender 0\n", "SeniorCitizen 0\n", "Partner 0\n", "Dependents 0\n", "tenure 0\n", "PhoneService 0\n", "MultipleLines 0\n", "InternetService 0\n", "OnlineSecurity 0\n", "OnlineBackup 0\n", "DeviceProtection 0\n", "TechSupport 0\n", "StreamingTV 0\n", "StreamingMovies 0\n", "Contract 0\n", "PaperlessBilling 0\n", "PaymentMethod 0\n", "MonthlyCharges 0\n", "TotalCharges 11\n", "Churn 0\n", "dtype: int64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['TotalCharges'] = pd.to_numeric(df['TotalCharges'], errors='coerce')\n", "df.isnull().sum() \n", "\n", "#Since TotalCharges can not be object so we need to convert it to numeric value first." ] }, { "cell_type": "code", "execution_count": 38, "id": "2eb4b0f8-1ae3-42a6-ad55-c1bf48b6f7da", "metadata": {}, "outputs": [], "source": [ "#We see that there are 11 rows with non-numeric value for the column TotalCharges so let us first drop all these 11 rows\n", "df.dropna(inplace=True)" ] }, { "cell_type": "code", "execution_count": 39, "id": "8ba63b7c-7144-451d-aca1-50aef9c41723", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "customerID 0\n", "gender 0\n", "SeniorCitizen 0\n", "Partner 0\n", "Dependents 0\n", "tenure 0\n", "PhoneService 0\n", "MultipleLines 0\n", "InternetService 0\n", "OnlineSecurity 0\n", "OnlineBackup 0\n", "DeviceProtection 0\n", "TechSupport 0\n", "StreamingTV 0\n", "StreamingMovies 0\n", "Contract 0\n", "PaperlessBilling 0\n", "PaymentMethod 0\n", "MonthlyCharges 0\n", "TotalCharges 0\n", "Churn 0\n", "dtype: int64" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 40, "id": "a3fcb4ae-13b7-4e36-ab4a-2b223f37d4ce", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countmeanstdmin25%50%75%max
SeniorCitizen7032.00.1624000.3688440.000.00000.0000.00001.00
tenure7032.032.42178624.5452601.009.000029.00055.000072.00
MonthlyCharges7032.064.79820830.08597418.2535.587570.35089.8625118.75
TotalCharges7032.02283.3004412266.77136218.80401.45001397.4753794.73758684.80
\n", "
" ], "text/plain": [ " count mean std min 25% 50% \\\n", "SeniorCitizen 7032.0 0.162400 0.368844 0.00 0.0000 0.000 \n", "tenure 7032.0 32.421786 24.545260 1.00 9.0000 29.000 \n", "MonthlyCharges 7032.0 64.798208 30.085974 18.25 35.5875 70.350 \n", "TotalCharges 7032.0 2283.300441 2266.771362 18.80 401.4500 1397.475 \n", "\n", " 75% max \n", "SeniorCitizen 0.0000 1.00 \n", "tenure 55.0000 72.00 \n", "MonthlyCharges 89.8625 118.75 \n", "TotalCharges 3794.7375 8684.80 " ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe().T" ] }, { "cell_type": "markdown", "id": "f16913b9-8f7d-4fae-a578-9d2e994fff2c", "metadata": {}, "source": [ "**B. Make sure all the variables with continuous values are of ‘Float’ type.**" ] }, { "cell_type": "code", "execution_count": 41, "id": "39c25404-5d75-495f-a24c-3a5984ab2b78", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Numerical Columns: ['SeniorCitizen', 'tenure', 'MonthlyCharges', 'TotalCharges']\n", "Categorical Columns: ['customerID', 'gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'Churn']\n", "\n", "\n", "\n", "Unique Values In Categorical columns:\n" ] }, { "data": { "text/plain": [ "[\"customerID: ['7590-VHVEG' '5575-GNVDE' '3668-QPYBK' ... '4801-JZAZL' '8361-LTMKD'\\n '3186-AJIEK']\",\n", " \"gender: ['Female' 'Male']\",\n", " \"Partner: ['Yes' 'No']\",\n", " \"Dependents: ['No' 'Yes']\",\n", " \"PhoneService: ['No' 'Yes']\",\n", " \"MultipleLines: ['No phone service' 'No' 'Yes']\",\n", " \"InternetService: ['DSL' 'Fiber optic' 'No']\",\n", " \"OnlineSecurity: ['No' 'Yes' 'No internet service']\",\n", " \"OnlineBackup: ['Yes' 'No' 'No internet service']\",\n", " \"DeviceProtection: ['No' 'Yes' 'No internet service']\",\n", " \"TechSupport: ['No' 'Yes' 'No internet service']\",\n", " \"StreamingTV: ['No' 'Yes' 'No internet service']\",\n", " \"StreamingMovies: ['No' 'Yes' 'No internet service']\",\n", " \"Contract: ['Month-to-month' 'One year' 'Two year']\",\n", " \"PaperlessBilling: ['Yes' 'No']\",\n", " \"PaymentMethod: ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\\n 'Credit card (automatic)']\",\n", " \"Churn: ['No' 'Yes']\"]" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "num_cols = df.select_dtypes(include='number')\n", "cat_cols = df.select_dtypes(include='object')\n", "print(f'Numerical Columns: {num_cols.columns.tolist()}')\n", "print(f'Categorical Columns: {cat_cols.columns.tolist()}\\n')\n", "print('\\n\\nUnique Values In Categorical columns:')\n", "[f'{col}: {cat_cols[col].unique()}' for col in cat_cols]" ] }, { "cell_type": "code", "execution_count": 42, "id": "b3d9ee0b-f476-41c6-8655-3bac20bd0f53", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "customerID object\n", "gender object\n", "SeniorCitizen int64\n", "Partner object\n", "Dependents object\n", "tenure int64\n", "PhoneService object\n", "MultipleLines object\n", "InternetService object\n", "OnlineSecurity object\n", "OnlineBackup object\n", "DeviceProtection object\n", "TechSupport object\n", "StreamingTV object\n", "StreamingMovies object\n", "Contract object\n", "PaperlessBilling object\n", "PaymentMethod object\n", "MonthlyCharges float64\n", "TotalCharges float64\n", "Churn object\n", "dtype: object" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": 43, "id": "cb92253d-f7ba-4c12-a07c-7639fbe10216", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "customerID object\n", "gender object\n", "SeniorCitizen float64\n", "Partner object\n", "Dependents object\n", "tenure float64\n", "PhoneService object\n", "MultipleLines object\n", "InternetService object\n", "OnlineSecurity object\n", "OnlineBackup object\n", "DeviceProtection object\n", "TechSupport object\n", "StreamingTV object\n", "StreamingMovies object\n", "Contract object\n", "PaperlessBilling object\n", "PaymentMethod object\n", "MonthlyCharges float64\n", "TotalCharges float64\n", "Churn object\n", "dtype: object" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df.astype({'SeniorCitizen':'float', 'tenure':'float'})\n", "df.dtypes" ] }, { "cell_type": "markdown", "id": "0e48d9c9-9213-4cb4-8bcf-621289b599eb", "metadata": {}, "source": [ "**C. Create a function that will accept a DataFrame as input and return pie-charts for all the appropriate Categorical features. Clearly show percentage\n", "distribution in the pie-chart.**" ] }, { "cell_type": "code", "execution_count": 44, "id": "3ba741fc-a2ce-4179-b937-476290836c7e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',\n", " 'tenure', 'PhoneService', 'MultipleLines', 'InternetService',\n", " 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',\n", " 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',\n", " 'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'],\n", " dtype='object')" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 45, "id": "524f42ff-584b-4e98-b82d-4f8279661ea8", "metadata": {}, "outputs": [], "source": [ "def pie_charts_for_CategoricalVar(df_pie,m):\n", " a = ['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents', 'tenure', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn']\n", " for i in df_pie:\n", " a.append(i)\n", " \n", " b = ['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'Churn']\n", " for i in a:\n", " if (df[i].dtype.name) == 'category':\n", " b.append(i)\n", " \n", " plt.figure(figsize=(15, 12))\n", " plt.subplots_adjust(hspace=0.2)\n", " plt.suptitle(\"Pie-Charts for Categorical Variables in the dataframe\", fontsize=18, y=0.95)\n", "\n", " ncols = m\n", "\n", " nrows = len(b) // ncols + (len(b) % ncols > 0)\n", " \n", " for n, i in enumerate(b):\n", " ax = plt.subplot(nrows, ncols, n + 1)\n", " df.groupby(i).size().plot(kind= 'pie' , autopct='%.2f%%',ax=ax)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 46, "id": "665b2570-1d7e-4df5-a4b4-72b27f67a847", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pie_charts_for_CategoricalVar(df,5)" ] }, { "cell_type": "markdown", "id": "6a9c71fb-0022-4a4e-9d88-056317d84ee0", "metadata": {}, "source": [ "**D. Share insights for Q2.c.**" ] }, { "cell_type": "markdown", "id": "e32cfe91-6896-4542-a19a-bd446309b576", "metadata": {}, "source": [ "*Majority of people who don't have partner also are dependents.*\n", "\n", "*Very less number of people who don't have partner are dependent.*\n", "\n", "*There are fairly equal number of people who have partner are dependent and not dependent.*\n", "\n", "*people taking longer contracts are likely to retain for a longer time.*\n", "\n", "*People whose payement methods are automatic(Bank transfer, Credit card) have more faith and confidence about the company and are likely to retain for a longer time.*\n", "\n", "*People who have partner are more likely to stay longer.*\n", "\n", "*Dependent status arguably does not have any strong correlation with tenure.*" ] }, { "cell_type": "markdown", "id": "9d007c2f-6fe8-4a35-91dc-785000a93f7f", "metadata": {}, "source": [ "**E. Encode all the appropriate Categorical features with the best suitable approach.**" ] }, { "cell_type": "code", "execution_count": 47, "id": "b357eff1-7f96-4765-a8e1-69488668ee31", "metadata": {}, "outputs": [], "source": [ "df.drop('customerID', axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 48, "id": "390fb252-5ca7-4d5b-9a8f-498c83cba3d8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 YesNo
Service-Service  
PhoneService-MultipleLines42.1928330.000000
PhoneService-OnlineSecurity24.6587035.674061
PhoneService-OnlineBackup30.4180895.602958
PhoneService-DeviceProtection30.0767925.361206
PhoneService-TechSupport24.9288965.588737
PhoneService-StreamingTV34.4141075.645620
PhoneService-StreamingMovies34.5989765.432309
MultipleLines-OnlineSecurity14.29180920.989761
MultipleLines-OnlineBackup19.29749720.236064
MultipleLines-DeviceProtection19.24061420.520478
MultipleLines-TechSupport14.49089920.918658
MultipleLines-StreamingTV22.41183219.354380
MultipleLines-StreamingMovies22.62514219.382821
OnlineSecurity-OnlineBackup15.96985231.214448
OnlineSecurity-DeviceProtection15.75654231.100683
OnlineSecurity-TechSupport15.58589336.305461
OnlineSecurity-StreamingTV14.87485826.166098
OnlineSecurity-StreamingMovies15.25881726.151877
OnlineBackup-DeviceProtection18.70022828.213879
OnlineBackup-TechSupport16.33959031.228669
OnlineBackup-StreamingTV19.76678025.227531
OnlineBackup-StreamingMovies19.75256024.815131
DeviceProtection-TechSupport17.15017132.138794
DeviceProtection-StreamingTV22.22696227.787258
DeviceProtection-StreamingMovies22.66780427.829920
TechSupport-StreamingTV17.27815728.213879
TechSupport-StreamingMovies17.46302628.000569
StreamingTV-StreamingMovies27.57394828.683163
\n" ], "text/plain": [ "" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "services = ['PhoneService', 'MultipleLines', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies']\n", "srv_corr = []\n", "percent_yes = []\n", "percent_no = []\n", "for i in range(len(services)-1):\n", " for j in range(i+1, len(services)):\n", " srv_corr.append(f'{services[i]}-{services[j]}')\n", " percent_yes.append(cat_cols.loc[(cat_cols[services[i]]=='Yes') & (cat_cols[services[j]]=='Yes')].shape[0]*100/cat_cols.shape[0])\n", " percent_no.append(cat_cols.loc[(cat_cols[services[i]]=='No') & (cat_cols[services[j]]=='No')].shape[0]*100/cat_cols.shape[0])\n", " \n", " \n", "service_corr = pd.DataFrame([srv_corr, percent_yes, percent_no], index=['Service-Service', 'Yes', 'No']).T\n", "service_corr = service_corr.set_index('Service-Service').style.background_gradient(subset=['Yes', 'No'], cmap='bone_r')\n", "service_corr" ] }, { "cell_type": "code", "execution_count": 49, "id": "decec0fd-d0be-49ec-8369-9dbea265e206", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['customerID', 'gender', 'Partner', 'Dependents', 'PhoneService',\n", " 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup',\n", " 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies',\n", " 'Contract', 'PaperlessBilling', 'PaymentMethod', 'Churn'],\n", " dtype='object')" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cat_cols.columns" ] }, { "cell_type": "code", "execution_count": 50, "id": "06640592-579f-4a74-a683-6d25c1c92151", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SeniorCitizentenureMonthlyChargesTotalChargesChurngender_Femalegender_MalePartner_NoPartner_YesDependents_No...StreamingMovies_YesContract_Month-to-monthContract_One yearContract_Two yearPaperlessBilling_NoPaperlessBilling_YesPaymentMethod_Bank transfer (automatic)PaymentMethod_Credit card (automatic)PaymentMethod_Electronic checkPaymentMethod_Mailed check
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40.02.070.70151.65110101...0100010010
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70380.024.084.801990.50001010...1010010001
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7032 rows × 46 columns

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" ], "text/plain": [ " SeniorCitizen tenure MonthlyCharges TotalCharges Churn \\\n", "0 0.0 1.0 29.85 29.85 0 \n", "1 0.0 34.0 56.95 1889.50 0 \n", "2 0.0 2.0 53.85 108.15 1 \n", "3 0.0 45.0 42.30 1840.75 0 \n", "4 0.0 2.0 70.70 151.65 1 \n", "... ... ... ... ... ... \n", "7038 0.0 24.0 84.80 1990.50 0 \n", "7039 0.0 72.0 103.20 7362.90 0 \n", "7040 0.0 11.0 29.60 346.45 0 \n", "7041 1.0 4.0 74.40 306.60 1 \n", "7042 0.0 66.0 105.65 6844.50 0 \n", "\n", " gender_Female gender_Male Partner_No Partner_Yes Dependents_No ... \\\n", "0 1 0 0 1 1 ... \n", "1 0 1 1 0 1 ... \n", "2 0 1 1 0 1 ... \n", "3 0 1 1 0 1 ... \n", "4 1 0 1 0 1 ... \n", "... ... ... ... ... ... ... \n", "7038 0 1 0 1 0 ... \n", "7039 1 0 0 1 0 ... \n", "7040 1 0 0 1 0 ... \n", "7041 0 1 0 1 1 ... \n", "7042 0 1 1 0 1 ... \n", "\n", " StreamingMovies_Yes Contract_Month-to-month Contract_One year \\\n", "0 0 1 0 \n", "1 0 0 1 \n", "2 0 1 0 \n", "3 0 0 1 \n", "4 0 1 0 \n", "... ... ... ... \n", "7038 1 0 1 \n", "7039 1 0 1 \n", "7040 0 1 0 \n", "7041 0 1 0 \n", "7042 1 0 0 \n", "\n", " Contract_Two year PaperlessBilling_No PaperlessBilling_Yes \\\n", "0 0 0 1 \n", "1 0 1 0 \n", "2 0 0 1 \n", "3 0 1 0 \n", "4 0 0 1 \n", "... ... ... ... \n", "7038 0 0 1 \n", "7039 0 0 1 \n", "7040 0 0 1 \n", "7041 0 0 1 \n", "7042 1 0 1 \n", "\n", " PaymentMethod_Bank transfer (automatic) \\\n", "0 0 \n", "1 0 \n", "2 0 \n", "3 1 \n", "4 0 \n", "... ... \n", "7038 0 \n", "7039 0 \n", "7040 0 \n", "7041 0 \n", "7042 1 \n", "\n", " PaymentMethod_Credit card (automatic) PaymentMethod_Electronic check \\\n", "0 0 1 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 1 \n", "... ... ... \n", "7038 0 0 \n", "7039 1 0 \n", "7040 0 1 \n", "7041 0 0 \n", "7042 0 0 \n", "\n", " PaymentMethod_Mailed check \n", "0 0 \n", "1 1 \n", "2 1 \n", "3 0 \n", "4 0 \n", "... ... \n", "7038 1 \n", "7039 0 \n", "7040 0 \n", "7041 1 \n", "7042 0 \n", "\n", "[7032 rows x 46 columns]" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "do_dummy_cols = ['gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines',\n", " 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection',\n", " 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract',\n", " 'PaperlessBilling', 'PaymentMethod']\n", "model_df = df.copy()\n", "model_df['Churn'].replace(to_replace='Yes', value=1, inplace=True)\n", "model_df['Churn'].replace(to_replace='No', value=0, inplace=True)\n", "model_df = pd.get_dummies(model_df, columns=do_dummy_cols)\n", "model_df" ] }, { "cell_type": "code", "execution_count": 51, "id": "cbc32ff1-2d6a-4019-b179-e57609c4c25d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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70380.024.084.801990.50001010...1010010001
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7032 rows × 46 columns

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" ], "text/plain": [ " SeniorCitizen tenure MonthlyCharges TotalCharges Churn \\\n", "0 0.0 1.0 29.85 29.85 0 \n", "1 0.0 34.0 56.95 1889.50 0 \n", "2 0.0 2.0 53.85 108.15 1 \n", "3 0.0 45.0 42.30 1840.75 0 \n", "4 0.0 2.0 70.70 151.65 1 \n", "... ... ... ... ... ... \n", "7038 0.0 24.0 84.80 1990.50 0 \n", "7039 0.0 72.0 103.20 7362.90 0 \n", "7040 0.0 11.0 29.60 346.45 0 \n", "7041 1.0 4.0 74.40 306.60 1 \n", "7042 0.0 66.0 105.65 6844.50 0 \n", "\n", " gender_Female gender_Male Partner_No Partner_Yes Dependents_No ... \\\n", "0 1 0 0 1 1 ... \n", "1 0 1 1 0 1 ... \n", "2 0 1 1 0 1 ... \n", "3 0 1 1 0 1 ... \n", "4 1 0 1 0 1 ... \n", "... ... ... ... ... ... ... \n", "7038 0 1 0 1 0 ... \n", "7039 1 0 0 1 0 ... \n", "7040 1 0 0 1 0 ... \n", "7041 0 1 0 1 1 ... \n", "7042 0 1 1 0 1 ... \n", "\n", " StreamingMovies_Yes Contract_Month-to-month Contract_One year \\\n", "0 0 1 0 \n", "1 0 0 1 \n", "2 0 1 0 \n", "3 0 0 1 \n", "4 0 1 0 \n", "... ... ... ... \n", "7038 1 0 1 \n", "7039 1 0 1 \n", "7040 0 1 0 \n", "7041 0 1 0 \n", "7042 1 0 0 \n", "\n", " Contract_Two year PaperlessBilling_No PaperlessBilling_Yes \\\n", "0 0 0 1 \n", "1 0 1 0 \n", "2 0 0 1 \n", "3 0 1 0 \n", "4 0 0 1 \n", "... ... ... ... \n", "7038 0 0 1 \n", "7039 0 0 1 \n", "7040 0 0 1 \n", "7041 0 0 1 \n", "7042 1 0 1 \n", "\n", " PaymentMethod_Bank transfer (automatic) \\\n", "0 0 \n", "1 0 \n", "2 0 \n", "3 1 \n", "4 0 \n", "... ... \n", "7038 0 \n", "7039 0 \n", "7040 0 \n", "7041 0 \n", "7042 1 \n", "\n", " PaymentMethod_Credit card (automatic) PaymentMethod_Electronic check \\\n", "0 0 1 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 1 \n", "... ... ... \n", "7038 0 0 \n", "7039 1 0 \n", "7040 0 1 \n", "7041 0 0 \n", "7042 0 0 \n", "\n", " PaymentMethod_Mailed check \n", "0 0 \n", "1 1 \n", "2 1 \n", "3 0 \n", "4 0 \n", "... ... \n", "7038 1 \n", "7039 0 \n", "7040 0 \n", "7041 1 \n", "7042 0 \n", "\n", "[7032 rows x 46 columns]" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_df" ] }, { "cell_type": "code", "execution_count": 52, "id": "24deffbe-6f5f-4685-8662-acfc840963ba", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countmeanstdmin25%50%75%max
SeniorCitizen7032.00.1624000.3688440.000.00000.0000.00001.00
tenure7032.032.42178624.5452601.009.000029.00055.000072.00
MonthlyCharges7032.064.79820830.08597418.2535.587570.35089.8625118.75
TotalCharges7032.02283.3004412266.77136218.80401.45001397.4753794.73758684.80
Churn7032.00.2657850.4417820.000.00000.0001.00001.00
gender_Female7032.00.4953070.5000140.000.00000.0001.00001.00
gender_Male7032.00.5046930.5000140.000.00001.0001.00001.00
Partner_No7032.00.5174910.4997290.000.00001.0001.00001.00
Partner_Yes7032.00.4825090.4997290.000.00000.0001.00001.00
Dependents_No7032.00.7015070.4576290.000.00001.0001.00001.00
Dependents_Yes7032.00.2984930.4576290.000.00000.0001.00001.00
PhoneService_No7032.00.0967010.2955710.000.00000.0000.00001.00
PhoneService_Yes7032.00.9032990.2955710.001.00001.0001.00001.00
MultipleLines_No7032.00.4813710.4996880.000.00000.0001.00001.00
MultipleLines_No phone service7032.00.0967010.2955710.000.00000.0000.00001.00
MultipleLines_Yes7032.00.4219280.4939020.000.00000.0001.00001.00
InternetService_DSL7032.00.3435720.4749340.000.00000.0001.00001.00
InternetService_Fiber optic7032.00.4402730.4964550.000.00000.0001.00001.00
InternetService_No7032.00.2161550.4116500.000.00000.0000.00001.00
OnlineSecurity_No7032.00.4972980.5000280.000.00000.0001.00001.00
OnlineSecurity_No internet service7032.00.2161550.4116500.000.00000.0000.00001.00
OnlineSecurity_Yes7032.00.2865470.4521800.000.00000.0001.00001.00
OnlineBackup_No7032.00.4389930.4963000.000.00000.0001.00001.00
OnlineBackup_No internet service7032.00.2161550.4116500.000.00000.0000.00001.00
OnlineBackup_Yes7032.00.3448520.4753540.000.00000.0001.00001.00
DeviceProtection_No7032.00.4399890.4964210.000.00000.0001.00001.00
DeviceProtection_No internet service7032.00.2161550.4116500.000.00000.0000.00001.00
DeviceProtection_Yes7032.00.3438570.4750280.000.00000.0001.00001.00
TechSupport_No7032.00.4937430.4999960.000.00000.0001.00001.00
TechSupport_No internet service7032.00.2161550.4116500.000.00000.0000.00001.00
TechSupport_Yes7032.00.2901020.4538420.000.00000.0001.00001.00
StreamingTV_No7032.00.3994600.4898220.000.00000.0001.00001.00
StreamingTV_No internet service7032.00.2161550.4116500.000.00000.0000.00001.00
StreamingTV_Yes7032.00.3843860.4864840.000.00000.0001.00001.00
StreamingMovies_No7032.00.3954780.4889880.000.00000.0001.00001.00
StreamingMovies_No internet service7032.00.2161550.4116500.000.00000.0000.00001.00
StreamingMovies_Yes7032.00.3883670.4874140.000.00000.0001.00001.00
Contract_Month-to-month7032.00.5510520.4974220.000.00001.0001.00001.00
Contract_One year7032.00.2093290.4068580.000.00000.0000.00001.00
Contract_Two year7032.00.2396190.4268810.000.00000.0000.00001.00
PaperlessBilling_No7032.00.4072810.4913630.000.00000.0001.00001.00
PaperlessBilling_Yes7032.00.5927190.4913630.000.00001.0001.00001.00
PaymentMethod_Bank transfer (automatic)7032.00.2192830.4137900.000.00000.0000.00001.00
PaymentMethod_Credit card (automatic)7032.00.2162970.4117480.000.00000.0000.00001.00
PaymentMethod_Electronic check7032.00.3363200.4724830.000.00000.0001.00001.00
PaymentMethod_Mailed check7032.00.2281000.4196370.000.00000.0000.00001.00
\n", "
" ], "text/plain": [ " count mean std \\\n", "SeniorCitizen 7032.0 0.162400 0.368844 \n", "tenure 7032.0 32.421786 24.545260 \n", "MonthlyCharges 7032.0 64.798208 30.085974 \n", "TotalCharges 7032.0 2283.300441 2266.771362 \n", "Churn 7032.0 0.265785 0.441782 \n", "gender_Female 7032.0 0.495307 0.500014 \n", "gender_Male 7032.0 0.504693 0.500014 \n", "Partner_No 7032.0 0.517491 0.499729 \n", "Partner_Yes 7032.0 0.482509 0.499729 \n", "Dependents_No 7032.0 0.701507 0.457629 \n", "Dependents_Yes 7032.0 0.298493 0.457629 \n", "PhoneService_No 7032.0 0.096701 0.295571 \n", "PhoneService_Yes 7032.0 0.903299 0.295571 \n", "MultipleLines_No 7032.0 0.481371 0.499688 \n", "MultipleLines_No phone service 7032.0 0.096701 0.295571 \n", "MultipleLines_Yes 7032.0 0.421928 0.493902 \n", "InternetService_DSL 7032.0 0.343572 0.474934 \n", "InternetService_Fiber optic 7032.0 0.440273 0.496455 \n", "InternetService_No 7032.0 0.216155 0.411650 \n", "OnlineSecurity_No 7032.0 0.497298 0.500028 \n", "OnlineSecurity_No internet service 7032.0 0.216155 0.411650 \n", "OnlineSecurity_Yes 7032.0 0.286547 0.452180 \n", "OnlineBackup_No 7032.0 0.438993 0.496300 \n", "OnlineBackup_No internet service 7032.0 0.216155 0.411650 \n", "OnlineBackup_Yes 7032.0 0.344852 0.475354 \n", "DeviceProtection_No 7032.0 0.439989 0.496421 \n", "DeviceProtection_No internet service 7032.0 0.216155 0.411650 \n", "DeviceProtection_Yes 7032.0 0.343857 0.475028 \n", "TechSupport_No 7032.0 0.493743 0.499996 \n", "TechSupport_No internet service 7032.0 0.216155 0.411650 \n", "TechSupport_Yes 7032.0 0.290102 0.453842 \n", "StreamingTV_No 7032.0 0.399460 0.489822 \n", "StreamingTV_No internet service 7032.0 0.216155 0.411650 \n", "StreamingTV_Yes 7032.0 0.384386 0.486484 \n", "StreamingMovies_No 7032.0 0.395478 0.488988 \n", "StreamingMovies_No internet service 7032.0 0.216155 0.411650 \n", "StreamingMovies_Yes 7032.0 0.388367 0.487414 \n", "Contract_Month-to-month 7032.0 0.551052 0.497422 \n", "Contract_One year 7032.0 0.209329 0.406858 \n", "Contract_Two year 7032.0 0.239619 0.426881 \n", "PaperlessBilling_No 7032.0 0.407281 0.491363 \n", "PaperlessBilling_Yes 7032.0 0.592719 0.491363 \n", "PaymentMethod_Bank transfer (automatic) 7032.0 0.219283 0.413790 \n", "PaymentMethod_Credit card (automatic) 7032.0 0.216297 0.411748 \n", "PaymentMethod_Electronic check 7032.0 0.336320 0.472483 \n", "PaymentMethod_Mailed check 7032.0 0.228100 0.419637 \n", "\n", " min 25% 50% 75% \\\n", "SeniorCitizen 0.00 0.0000 0.000 0.0000 \n", "tenure 1.00 9.0000 29.000 55.0000 \n", "MonthlyCharges 18.25 35.5875 70.350 89.8625 \n", "TotalCharges 18.80 401.4500 1397.475 3794.7375 \n", "Churn 0.00 0.0000 0.000 1.0000 \n", "gender_Female 0.00 0.0000 0.000 1.0000 \n", "gender_Male 0.00 0.0000 1.000 1.0000 \n", "Partner_No 0.00 0.0000 1.000 1.0000 \n", "Partner_Yes 0.00 0.0000 0.000 1.0000 \n", "Dependents_No 0.00 0.0000 1.000 1.0000 \n", "Dependents_Yes 0.00 0.0000 0.000 1.0000 \n", "PhoneService_No 0.00 0.0000 0.000 0.0000 \n", "PhoneService_Yes 0.00 1.0000 1.000 1.0000 \n", "MultipleLines_No 0.00 0.0000 0.000 1.0000 \n", "MultipleLines_No phone service 0.00 0.0000 0.000 0.0000 \n", "MultipleLines_Yes 0.00 0.0000 0.000 1.0000 \n", "InternetService_DSL 0.00 0.0000 0.000 1.0000 \n", "InternetService_Fiber optic 0.00 0.0000 0.000 1.0000 \n", "InternetService_No 0.00 0.0000 0.000 0.0000 \n", "OnlineSecurity_No 0.00 0.0000 0.000 1.0000 \n", "OnlineSecurity_No internet service 0.00 0.0000 0.000 0.0000 \n", "OnlineSecurity_Yes 0.00 0.0000 0.000 1.0000 \n", "OnlineBackup_No 0.00 0.0000 0.000 1.0000 \n", "OnlineBackup_No internet service 0.00 0.0000 0.000 0.0000 \n", "OnlineBackup_Yes 0.00 0.0000 0.000 1.0000 \n", "DeviceProtection_No 0.00 0.0000 0.000 1.0000 \n", "DeviceProtection_No internet service 0.00 0.0000 0.000 0.0000 \n", "DeviceProtection_Yes 0.00 0.0000 0.000 1.0000 \n", "TechSupport_No 0.00 0.0000 0.000 1.0000 \n", "TechSupport_No internet service 0.00 0.0000 0.000 0.0000 \n", "TechSupport_Yes 0.00 0.0000 0.000 1.0000 \n", "StreamingTV_No 0.00 0.0000 0.000 1.0000 \n", "StreamingTV_No internet service 0.00 0.0000 0.000 0.0000 \n", "StreamingTV_Yes 0.00 0.0000 0.000 1.0000 \n", "StreamingMovies_No 0.00 0.0000 0.000 1.0000 \n", "StreamingMovies_No internet service 0.00 0.0000 0.000 0.0000 \n", "StreamingMovies_Yes 0.00 0.0000 0.000 1.0000 \n", "Contract_Month-to-month 0.00 0.0000 1.000 1.0000 \n", "Contract_One year 0.00 0.0000 0.000 0.0000 \n", "Contract_Two year 0.00 0.0000 0.000 0.0000 \n", "PaperlessBilling_No 0.00 0.0000 0.000 1.0000 \n", "PaperlessBilling_Yes 0.00 0.0000 1.000 1.0000 \n", "PaymentMethod_Bank transfer (automatic) 0.00 0.0000 0.000 0.0000 \n", "PaymentMethod_Credit card (automatic) 0.00 0.0000 0.000 0.0000 \n", "PaymentMethod_Electronic check 0.00 0.0000 0.000 1.0000 \n", "PaymentMethod_Mailed check 0.00 0.0000 0.000 0.0000 \n", "\n", " max \n", "SeniorCitizen 1.00 \n", "tenure 72.00 \n", "MonthlyCharges 118.75 \n", "TotalCharges 8684.80 \n", "Churn 1.00 \n", "gender_Female 1.00 \n", "gender_Male 1.00 \n", "Partner_No 1.00 \n", "Partner_Yes 1.00 \n", "Dependents_No 1.00 \n", "Dependents_Yes 1.00 \n", "PhoneService_No 1.00 \n", "PhoneService_Yes 1.00 \n", "MultipleLines_No 1.00 \n", "MultipleLines_No phone service 1.00 \n", "MultipleLines_Yes 1.00 \n", "InternetService_DSL 1.00 \n", "InternetService_Fiber optic 1.00 \n", "InternetService_No 1.00 \n", "OnlineSecurity_No 1.00 \n", "OnlineSecurity_No internet service 1.00 \n", "OnlineSecurity_Yes 1.00 \n", "OnlineBackup_No 1.00 \n", "OnlineBackup_No internet service 1.00 \n", "OnlineBackup_Yes 1.00 \n", "DeviceProtection_No 1.00 \n", "DeviceProtection_No internet service 1.00 \n", "DeviceProtection_Yes 1.00 \n", "TechSupport_No 1.00 \n", "TechSupport_No internet service 1.00 \n", "TechSupport_Yes 1.00 \n", "StreamingTV_No 1.00 \n", "StreamingTV_No internet service 1.00 \n", "StreamingTV_Yes 1.00 \n", "StreamingMovies_No 1.00 \n", "StreamingMovies_No internet service 1.00 \n", "StreamingMovies_Yes 1.00 \n", "Contract_Month-to-month 1.00 \n", "Contract_One year 1.00 \n", "Contract_Two year 1.00 \n", "PaperlessBilling_No 1.00 \n", "PaperlessBilling_Yes 1.00 \n", "PaymentMethod_Bank transfer (automatic) 1.00 \n", "PaymentMethod_Credit card (automatic) 1.00 \n", "PaymentMethod_Electronic check 1.00 \n", "PaymentMethod_Mailed check 1.00 " ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_df.describe().T" ] }, { "cell_type": "code", "execution_count": 53, "id": "1f262c7f-0437-4b65-9827-ab4f893aeb8e", "metadata": {}, "outputs": [], "source": [ "model_df.drop(columns=['tenure'], inplace=True)\n", "y = model_df['Churn']\n", "X = model_df.drop(columns=['Churn'])" ] }, { "cell_type": "code", "execution_count": 54, "id": "72a9a2c4-49de-4174-8056-e76372115cc7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countmeanstdmin25%50%75%max
SeniorCitizen7032.00.1624000.3688440.000.00000.0000.00001.00
MonthlyCharges7032.064.79820830.08597418.2535.587570.35089.8625118.75
TotalCharges7032.02283.3004412266.77136218.80401.45001397.4753794.73758684.80
Churn7032.00.2657850.4417820.000.00000.0001.00001.00
gender_Female7032.00.4953070.5000140.000.00000.0001.00001.00
gender_Male7032.00.5046930.5000140.000.00001.0001.00001.00
Partner_No7032.00.5174910.4997290.000.00001.0001.00001.00
Partner_Yes7032.00.4825090.4997290.000.00000.0001.00001.00
Dependents_No7032.00.7015070.4576290.000.00001.0001.00001.00
Dependents_Yes7032.00.2984930.4576290.000.00000.0001.00001.00
PhoneService_No7032.00.0967010.2955710.000.00000.0000.00001.00
PhoneService_Yes7032.00.9032990.2955710.001.00001.0001.00001.00
MultipleLines_No7032.00.4813710.4996880.000.00000.0001.00001.00
MultipleLines_No phone service7032.00.0967010.2955710.000.00000.0000.00001.00
MultipleLines_Yes7032.00.4219280.4939020.000.00000.0001.00001.00
InternetService_DSL7032.00.3435720.4749340.000.00000.0001.00001.00
InternetService_Fiber optic7032.00.4402730.4964550.000.00000.0001.00001.00
InternetService_No7032.00.2161550.4116500.000.00000.0000.00001.00
OnlineSecurity_No7032.00.4972980.5000280.000.00000.0001.00001.00
OnlineSecurity_No internet service7032.00.2161550.4116500.000.00000.0000.00001.00
OnlineSecurity_Yes7032.00.2865470.4521800.000.00000.0001.00001.00
OnlineBackup_No7032.00.4389930.4963000.000.00000.0001.00001.00
OnlineBackup_No internet service7032.00.2161550.4116500.000.00000.0000.00001.00
OnlineBackup_Yes7032.00.3448520.4753540.000.00000.0001.00001.00
DeviceProtection_No7032.00.4399890.4964210.000.00000.0001.00001.00
DeviceProtection_No internet service7032.00.2161550.4116500.000.00000.0000.00001.00
DeviceProtection_Yes7032.00.3438570.4750280.000.00000.0001.00001.00
TechSupport_No7032.00.4937430.4999960.000.00000.0001.00001.00
TechSupport_No internet service7032.00.2161550.4116500.000.00000.0000.00001.00
TechSupport_Yes7032.00.2901020.4538420.000.00000.0001.00001.00
StreamingTV_No7032.00.3994600.4898220.000.00000.0001.00001.00
StreamingTV_No internet service7032.00.2161550.4116500.000.00000.0000.00001.00
StreamingTV_Yes7032.00.3843860.4864840.000.00000.0001.00001.00
StreamingMovies_No7032.00.3954780.4889880.000.00000.0001.00001.00
StreamingMovies_No internet service7032.00.2161550.4116500.000.00000.0000.00001.00
StreamingMovies_Yes7032.00.3883670.4874140.000.00000.0001.00001.00
Contract_Month-to-month7032.00.5510520.4974220.000.00001.0001.00001.00
Contract_One year7032.00.2093290.4068580.000.00000.0000.00001.00
Contract_Two year7032.00.2396190.4268810.000.00000.0000.00001.00
PaperlessBilling_No7032.00.4072810.4913630.000.00000.0001.00001.00
PaperlessBilling_Yes7032.00.5927190.4913630.000.00001.0001.00001.00
PaymentMethod_Bank transfer (automatic)7032.00.2192830.4137900.000.00000.0000.00001.00
PaymentMethod_Credit card (automatic)7032.00.2162970.4117480.000.00000.0000.00001.00
PaymentMethod_Electronic check7032.00.3363200.4724830.000.00000.0001.00001.00
PaymentMethod_Mailed check7032.00.2281000.4196370.000.00000.0000.00001.00
\n", "
" ], "text/plain": [ " count mean std \\\n", "SeniorCitizen 7032.0 0.162400 0.368844 \n", "MonthlyCharges 7032.0 64.798208 30.085974 \n", "TotalCharges 7032.0 2283.300441 2266.771362 \n", "Churn 7032.0 0.265785 0.441782 \n", "gender_Female 7032.0 0.495307 0.500014 \n", "gender_Male 7032.0 0.504693 0.500014 \n", "Partner_No 7032.0 0.517491 0.499729 \n", "Partner_Yes 7032.0 0.482509 0.499729 \n", "Dependents_No 7032.0 0.701507 0.457629 \n", "Dependents_Yes 7032.0 0.298493 0.457629 \n", "PhoneService_No 7032.0 0.096701 0.295571 \n", "PhoneService_Yes 7032.0 0.903299 0.295571 \n", "MultipleLines_No 7032.0 0.481371 0.499688 \n", "MultipleLines_No phone service 7032.0 0.096701 0.295571 \n", "MultipleLines_Yes 7032.0 0.421928 0.493902 \n", "InternetService_DSL 7032.0 0.343572 0.474934 \n", "InternetService_Fiber optic 7032.0 0.440273 0.496455 \n", "InternetService_No 7032.0 0.216155 0.411650 \n", "OnlineSecurity_No 7032.0 0.497298 0.500028 \n", "OnlineSecurity_No internet service 7032.0 0.216155 0.411650 \n", "OnlineSecurity_Yes 7032.0 0.286547 0.452180 \n", "OnlineBackup_No 7032.0 0.438993 0.496300 \n", "OnlineBackup_No internet service 7032.0 0.216155 0.411650 \n", "OnlineBackup_Yes 7032.0 0.344852 0.475354 \n", "DeviceProtection_No 7032.0 0.439989 0.496421 \n", "DeviceProtection_No internet service 7032.0 0.216155 0.411650 \n", "DeviceProtection_Yes 7032.0 0.343857 0.475028 \n", "TechSupport_No 7032.0 0.493743 0.499996 \n", "TechSupport_No internet service 7032.0 0.216155 0.411650 \n", "TechSupport_Yes 7032.0 0.290102 0.453842 \n", "StreamingTV_No 7032.0 0.399460 0.489822 \n", "StreamingTV_No internet service 7032.0 0.216155 0.411650 \n", "StreamingTV_Yes 7032.0 0.384386 0.486484 \n", "StreamingMovies_No 7032.0 0.395478 0.488988 \n", "StreamingMovies_No internet service 7032.0 0.216155 0.411650 \n", "StreamingMovies_Yes 7032.0 0.388367 0.487414 \n", "Contract_Month-to-month 7032.0 0.551052 0.497422 \n", "Contract_One year 7032.0 0.209329 0.406858 \n", "Contract_Two year 7032.0 0.239619 0.426881 \n", "PaperlessBilling_No 7032.0 0.407281 0.491363 \n", "PaperlessBilling_Yes 7032.0 0.592719 0.491363 \n", "PaymentMethod_Bank transfer (automatic) 7032.0 0.219283 0.413790 \n", "PaymentMethod_Credit card (automatic) 7032.0 0.216297 0.411748 \n", "PaymentMethod_Electronic check 7032.0 0.336320 0.472483 \n", "PaymentMethod_Mailed check 7032.0 0.228100 0.419637 \n", "\n", " min 25% 50% 75% \\\n", "SeniorCitizen 0.00 0.0000 0.000 0.0000 \n", "MonthlyCharges 18.25 35.5875 70.350 89.8625 \n", "TotalCharges 18.80 401.4500 1397.475 3794.7375 \n", "Churn 0.00 0.0000 0.000 1.0000 \n", "gender_Female 0.00 0.0000 0.000 1.0000 \n", "gender_Male 0.00 0.0000 1.000 1.0000 \n", "Partner_No 0.00 0.0000 1.000 1.0000 \n", "Partner_Yes 0.00 0.0000 0.000 1.0000 \n", "Dependents_No 0.00 0.0000 1.000 1.0000 \n", "Dependents_Yes 0.00 0.0000 0.000 1.0000 \n", "PhoneService_No 0.00 0.0000 0.000 0.0000 \n", "PhoneService_Yes 0.00 1.0000 1.000 1.0000 \n", "MultipleLines_No 0.00 0.0000 0.000 1.0000 \n", "MultipleLines_No phone service 0.00 0.0000 0.000 0.0000 \n", "MultipleLines_Yes 0.00 0.0000 0.000 1.0000 \n", "InternetService_DSL 0.00 0.0000 0.000 1.0000 \n", "InternetService_Fiber optic 0.00 0.0000 0.000 1.0000 \n", "InternetService_No 0.00 0.0000 0.000 0.0000 \n", "OnlineSecurity_No 0.00 0.0000 0.000 1.0000 \n", "OnlineSecurity_No internet service 0.00 0.0000 0.000 0.0000 \n", "OnlineSecurity_Yes 0.00 0.0000 0.000 1.0000 \n", "OnlineBackup_No 0.00 0.0000 0.000 1.0000 \n", "OnlineBackup_No internet service 0.00 0.0000 0.000 0.0000 \n", "OnlineBackup_Yes 0.00 0.0000 0.000 1.0000 \n", "DeviceProtection_No 0.00 0.0000 0.000 1.0000 \n", "DeviceProtection_No internet service 0.00 0.0000 0.000 0.0000 \n", "DeviceProtection_Yes 0.00 0.0000 0.000 1.0000 \n", "TechSupport_No 0.00 0.0000 0.000 1.0000 \n", "TechSupport_No internet service 0.00 0.0000 0.000 0.0000 \n", "TechSupport_Yes 0.00 0.0000 0.000 1.0000 \n", "StreamingTV_No 0.00 0.0000 0.000 1.0000 \n", "StreamingTV_No internet service 0.00 0.0000 0.000 0.0000 \n", "StreamingTV_Yes 0.00 0.0000 0.000 1.0000 \n", "StreamingMovies_No 0.00 0.0000 0.000 1.0000 \n", "StreamingMovies_No internet service 0.00 0.0000 0.000 0.0000 \n", "StreamingMovies_Yes 0.00 0.0000 0.000 1.0000 \n", "Contract_Month-to-month 0.00 0.0000 1.000 1.0000 \n", "Contract_One year 0.00 0.0000 0.000 0.0000 \n", "Contract_Two year 0.00 0.0000 0.000 0.0000 \n", "PaperlessBilling_No 0.00 0.0000 0.000 1.0000 \n", "PaperlessBilling_Yes 0.00 0.0000 1.000 1.0000 \n", "PaymentMethod_Bank transfer (automatic) 0.00 0.0000 0.000 0.0000 \n", "PaymentMethod_Credit card (automatic) 0.00 0.0000 0.000 0.0000 \n", "PaymentMethod_Electronic check 0.00 0.0000 0.000 1.0000 \n", "PaymentMethod_Mailed check 0.00 0.0000 0.000 0.0000 \n", "\n", " max \n", "SeniorCitizen 1.00 \n", "MonthlyCharges 118.75 \n", "TotalCharges 8684.80 \n", "Churn 1.00 \n", "gender_Female 1.00 \n", "gender_Male 1.00 \n", "Partner_No 1.00 \n", "Partner_Yes 1.00 \n", "Dependents_No 1.00 \n", "Dependents_Yes 1.00 \n", "PhoneService_No 1.00 \n", "PhoneService_Yes 1.00 \n", "MultipleLines_No 1.00 \n", "MultipleLines_No phone service 1.00 \n", "MultipleLines_Yes 1.00 \n", "InternetService_DSL 1.00 \n", "InternetService_Fiber optic 1.00 \n", "InternetService_No 1.00 \n", "OnlineSecurity_No 1.00 \n", "OnlineSecurity_No internet service 1.00 \n", "OnlineSecurity_Yes 1.00 \n", "OnlineBackup_No 1.00 \n", "OnlineBackup_No internet service 1.00 \n", "OnlineBackup_Yes 1.00 \n", "DeviceProtection_No 1.00 \n", "DeviceProtection_No internet service 1.00 \n", "DeviceProtection_Yes 1.00 \n", "TechSupport_No 1.00 \n", "TechSupport_No internet service 1.00 \n", "TechSupport_Yes 1.00 \n", "StreamingTV_No 1.00 \n", "StreamingTV_No internet service 1.00 \n", "StreamingTV_Yes 1.00 \n", "StreamingMovies_No 1.00 \n", "StreamingMovies_No internet service 1.00 \n", "StreamingMovies_Yes 1.00 \n", "Contract_Month-to-month 1.00 \n", "Contract_One year 1.00 \n", "Contract_Two year 1.00 \n", "PaperlessBilling_No 1.00 \n", "PaperlessBilling_Yes 1.00 \n", "PaymentMethod_Bank transfer (automatic) 1.00 \n", "PaymentMethod_Credit card (automatic) 1.00 \n", "PaymentMethod_Electronic check 1.00 \n", "PaymentMethod_Mailed check 1.00 " ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_df.describe().T" ] }, { "cell_type": "markdown", "id": "0a2e4a2e-52d0-48b7-b1da-0c4ab01d3746", "metadata": {}, "source": [ "**F. Split the data into 80% train and 20% test.**" ] }, { "cell_type": "code", "execution_count": 55, "id": "c81b4418-366b-4049-83fb-baaa7083fb7f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Churn 1.000000\n", "Contract_Month-to-month 0.404565\n", "OnlineSecurity_No 0.342235\n", "TechSupport_No 0.336877\n", "InternetService_Fiber optic 0.307463\n", "PaymentMethod_Electronic check 0.301455\n", "OnlineBackup_No 0.267595\n", "DeviceProtection_No 0.252056\n", "MonthlyCharges 0.192858\n", "PaperlessBilling_Yes 0.191454\n", "Dependents_No 0.163128\n", "SeniorCitizen 0.150541\n", "Partner_No 0.149982\n", "StreamingMovies_No 0.130920\n", "StreamingTV_No 0.128435\n", "StreamingTV_Yes 0.063254\n", "StreamingMovies_Yes 0.060860\n", "MultipleLines_Yes 0.040033\n", "PhoneService_Yes 0.011691\n", "gender_Female 0.008545\n", "gender_Male -0.008545\n", "PhoneService_No -0.011691\n", "MultipleLines_No phone service -0.011691\n", "MultipleLines_No -0.032654\n", "DeviceProtection_Yes -0.066193\n", "OnlineBackup_Yes -0.082307\n", "PaymentMethod_Mailed check -0.090773\n", "PaymentMethod_Bank transfer (automatic) -0.118136\n", "InternetService_DSL -0.124141\n", "PaymentMethod_Credit card (automatic) -0.134687\n", "Partner_Yes -0.149982\n", "Dependents_Yes -0.163128\n", "TechSupport_Yes -0.164716\n", "OnlineSecurity_Yes -0.171270\n", "Contract_One year -0.178225\n", "PaperlessBilling_No -0.191454\n", "TotalCharges -0.199484\n", "DeviceProtection_No internet service -0.227578\n", "TechSupport_No internet service -0.227578\n", "StreamingMovies_No internet service -0.227578\n", "InternetService_No -0.227578\n", "OnlineSecurity_No internet service -0.227578\n", "StreamingTV_No internet service -0.227578\n", "OnlineBackup_No internet service -0.227578\n", "Contract_Two year -0.301552\n", "Name: Churn, dtype: float64" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "feature_corr = model_df.corr()['Churn'].sort_values(ascending=False)\n", "feature_corr" ] }, { "cell_type": "code", "execution_count": 56, "id": "29e3dc4d-b80a-47ce-88cb-f1fbd2a77866", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SeniorCitizenMonthlyChargesTotalChargesChurngender_Femalegender_MalePartner_NoPartner_YesDependents_NoDependents_Yes...StreamingMovies_YesContract_Month-to-monthContract_One yearContract_Two yearPaperlessBilling_NoPaperlessBilling_YesPaymentMethod_Bank transfer (automatic)PaymentMethod_Credit card (automatic)PaymentMethod_Electronic checkPaymentMethod_Mailed check
00.029.8529.850100110...0100010010
10.056.951889.500011010...0010100001
20.053.85108.151011010...0100010001
30.042.301840.750011010...0010101000
40.070.70151.651101010...0100010010
..................................................................
70380.084.801990.500010101...1010010001
70390.0103.207362.900100101...1010010100
70400.029.60346.450100101...0100010010
70411.074.40306.601010110...0100010001
70420.0105.656844.500011010...1001011000
\n", "

7032 rows × 45 columns

\n", "
" ], "text/plain": [ " SeniorCitizen MonthlyCharges TotalCharges Churn gender_Female \\\n", "0 0.0 29.85 29.85 0 1 \n", "1 0.0 56.95 1889.50 0 0 \n", "2 0.0 53.85 108.15 1 0 \n", "3 0.0 42.30 1840.75 0 0 \n", "4 0.0 70.70 151.65 1 1 \n", "... ... ... ... ... ... \n", "7038 0.0 84.80 1990.50 0 0 \n", "7039 0.0 103.20 7362.90 0 1 \n", "7040 0.0 29.60 346.45 0 1 \n", "7041 1.0 74.40 306.60 1 0 \n", "7042 0.0 105.65 6844.50 0 0 \n", "\n", " gender_Male Partner_No Partner_Yes Dependents_No Dependents_Yes \\\n", "0 0 0 1 1 0 \n", "1 1 1 0 1 0 \n", "2 1 1 0 1 0 \n", "3 1 1 0 1 0 \n", "4 0 1 0 1 0 \n", "... ... ... ... ... ... \n", "7038 1 0 1 0 1 \n", "7039 0 0 1 0 1 \n", "7040 0 0 1 0 1 \n", "7041 1 0 1 1 0 \n", "7042 1 1 0 1 0 \n", "\n", " ... StreamingMovies_Yes Contract_Month-to-month Contract_One year \\\n", "0 ... 0 1 0 \n", "1 ... 0 0 1 \n", "2 ... 0 1 0 \n", "3 ... 0 0 1 \n", "4 ... 0 1 0 \n", "... ... ... ... ... \n", "7038 ... 1 0 1 \n", "7039 ... 1 0 1 \n", "7040 ... 0 1 0 \n", "7041 ... 0 1 0 \n", "7042 ... 1 0 0 \n", "\n", " Contract_Two year PaperlessBilling_No PaperlessBilling_Yes \\\n", "0 0 0 1 \n", "1 0 1 0 \n", "2 0 0 1 \n", "3 0 1 0 \n", "4 0 0 1 \n", "... ... ... ... \n", "7038 0 0 1 \n", "7039 0 0 1 \n", "7040 0 0 1 \n", "7041 0 0 1 \n", "7042 1 0 1 \n", "\n", " PaymentMethod_Bank transfer (automatic) \\\n", "0 0 \n", "1 0 \n", "2 0 \n", "3 1 \n", "4 0 \n", "... ... \n", "7038 0 \n", "7039 0 \n", "7040 0 \n", "7041 0 \n", "7042 1 \n", "\n", " PaymentMethod_Credit card (automatic) PaymentMethod_Electronic check \\\n", "0 0 1 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 1 \n", "... ... ... \n", "7038 0 0 \n", "7039 1 0 \n", "7040 0 1 \n", "7041 0 0 \n", "7042 0 0 \n", "\n", " PaymentMethod_Mailed check \n", "0 0 \n", "1 1 \n", "2 1 \n", "3 0 \n", "4 0 \n", "... ... \n", "7038 1 \n", "7039 0 \n", "7040 0 \n", "7041 1 \n", "7042 0 \n", "\n", "[7032 rows x 45 columns]" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_df" ] }, { "cell_type": "code", "execution_count": 57, "id": "9a5efac6-aab1-4cf8-99bc-35716743dd83", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['SeniorCitizen', 'MonthlyCharges', 'TotalCharges', 'Churn',\n", " 'gender_Female', 'gender_Male', 'Partner_No', 'Partner_Yes',\n", " 'Dependents_No', 'Dependents_Yes', 'PhoneService_No',\n", " 'PhoneService_Yes', 'MultipleLines_No',\n", " 'MultipleLines_No phone service', 'MultipleLines_Yes',\n", " 'InternetService_DSL', 'InternetService_Fiber optic',\n", " 'InternetService_No', 'OnlineSecurity_No',\n", " 'OnlineSecurity_No internet service', 'OnlineSecurity_Yes',\n", " 'OnlineBackup_No', 'OnlineBackup_No internet service',\n", " 'OnlineBackup_Yes', 'DeviceProtection_No',\n", " 'DeviceProtection_No internet service', 'DeviceProtection_Yes',\n", " 'TechSupport_No', 'TechSupport_No internet service', 'TechSupport_Yes',\n", " 'StreamingTV_No', 'StreamingTV_No internet service', 'StreamingTV_Yes',\n", " 'StreamingMovies_No', 'StreamingMovies_No internet service',\n", " 'StreamingMovies_Yes', 'Contract_Month-to-month', 'Contract_One year',\n", " 'Contract_Two year', 'PaperlessBilling_No', 'PaperlessBilling_Yes',\n", " 'PaymentMethod_Bank transfer (automatic)',\n", " 'PaymentMethod_Credit card (automatic)',\n", " 'PaymentMethod_Electronic check', 'PaymentMethod_Mailed check'],\n", " dtype='object')" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_df.columns" ] }, { "cell_type": "code", "execution_count": 58, "id": "7f228db2-b38a-4651-846c-677490792195", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "\"['tenure'] not found in axis\"", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "Input \u001b[1;32mIn [58]\u001b[0m, in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mmodel_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtenure\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m 2\u001b[0m y \u001b[38;5;241m=\u001b[39m model_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mChurn\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m 3\u001b[0m X \u001b[38;5;241m=\u001b[39m model_df\u001b[38;5;241m.\u001b[39mdrop(columns\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mChurn\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py:311\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments..decorate..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[0;32m 306\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m 307\u001b[0m msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39marguments),\n\u001b[0;32m 308\u001b[0m \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[0;32m 309\u001b[0m stacklevel\u001b[38;5;241m=\u001b[39mstacklevel,\n\u001b[0;32m 310\u001b[0m )\n\u001b[1;32m--> 311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4954\u001b[0m, in \u001b[0;36mDataFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m 4806\u001b[0m \u001b[38;5;129m@deprecate_nonkeyword_arguments\u001b[39m(version\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, allowed_args\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m 4807\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdrop\u001b[39m(\n\u001b[0;32m 4808\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 4815\u001b[0m errors: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 4816\u001b[0m ):\n\u001b[0;32m 4817\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 4818\u001b[0m \u001b[38;5;124;03m Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[0;32m 4819\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 4952\u001b[0m \u001b[38;5;124;03m weight 1.0 0.8\u001b[39;00m\n\u001b[0;32m 4953\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 4954\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 4955\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 4956\u001b[0m \u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 4957\u001b[0m \u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 4958\u001b[0m \u001b[43m \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 4959\u001b[0m \u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 4960\u001b[0m \u001b[43m \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 4961\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 4962\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:4267\u001b[0m, in \u001b[0;36mNDFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m 4265\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, labels \u001b[38;5;129;01min\u001b[39;00m axes\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m 4266\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 4267\u001b[0m obj \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_drop_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4269\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[0;32m 4270\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_inplace(obj)\n", "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:4311\u001b[0m, in \u001b[0;36mNDFrame._drop_axis\u001b[1;34m(self, labels, axis, level, errors, consolidate, only_slice)\u001b[0m\n\u001b[0;32m 4309\u001b[0m new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m 4310\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 4311\u001b[0m new_axis \u001b[38;5;241m=\u001b[39m \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4312\u001b[0m indexer \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mget_indexer(new_axis)\n\u001b[0;32m 4314\u001b[0m \u001b[38;5;66;03m# Case for non-unique axis\u001b[39;00m\n\u001b[0;32m 4315\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", "File \u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py:6644\u001b[0m, in \u001b[0;36mIndex.drop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m 6642\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[0;32m 6643\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m-> 6644\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(labels[mask])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found in axis\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 6645\u001b[0m indexer \u001b[38;5;241m=\u001b[39m indexer[\u001b[38;5;241m~\u001b[39mmask]\n\u001b[0;32m 6646\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelete(indexer)\n", "\u001b[1;31mKeyError\u001b[0m: \"['tenure'] not found in axis\"" ] } ], "source": [ "model_df.drop(columns=['tenure'], inplace=True)\n", "y = model_df['Churn']\n", "X = model_df.drop(columns=['Churn'])" ] }, { "cell_type": "code", "execution_count": null, "id": "3a08b867-afeb-4284-94be-730575e3a126", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "7b17e6f3-7be2-4331-b052-2a7c874f39c0", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 59, "id": "9efe3166-0c03-4524-95bd-e899cbe47356", "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)" ] }, { "cell_type": "code", "execution_count": 60, "id": "aaf2571c-817c-4381-b7c0-00fbc09f3224", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 4130\n", "1 1495\n", "Name: Churn, dtype: int64" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train.value_counts()" ] }, { "cell_type": "markdown", "id": "e0c4bbfd-6881-428f-a6f6-292738cd9388", "metadata": {}, "source": [ "**G. Normalize/Standardize the data with the best suitable approach.**" ] }, { "cell_type": "code", "execution_count": 61, "id": "3ab58c87-fb82-4529-b8b9-24c3ed7a568d", "metadata": {}, "outputs": [], "source": [ "from sklearn.decomposition import PCA" ] }, { "cell_type": "code", "execution_count": 62, "id": "263c984c-54b0-40ea-b3d5-9d18b17c2b38", "metadata": {}, "outputs": [], "source": [ "sc = StandardScaler()\n", "X_train= sc.fit_transform(X_train)\n", "X_test= sc.transform(X_test)" ] }, { "cell_type": "code", "execution_count": 63, "id": "5553c92b-ca6a-4cd6-969e-fe9bf0830f23", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-0.43931886, 0.98155578, 1.6599004 , ..., 1.90515869,\n", " -0.71617747, -0.5394682 ],\n", " [-0.43931886, -0.97154551, -0.56225219, ..., -0.52489066,\n", " 1.39630196, -0.5394682 ],\n", " [-0.43931886, 0.83706615, 1.75610395, ..., 1.90515869,\n", " -0.71617747, -0.5394682 ],\n", " ...,\n", " [-0.43931886, 0.92674937, 0.47350714, ..., 1.90515869,\n", " -0.71617747, -0.5394682 ],\n", " [-0.43931886, 0.02991714, -0.721544 , ..., -0.52489066,\n", " -0.71617747, 1.85367739],\n", " [ 2.27625101, 0.32221802, -0.9787973 , ..., -0.52489066,\n", " 1.39630196, -0.5394682 ]])" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train" ] }, { "cell_type": "markdown", "id": "ea5c1a97-9fa8-447a-bbcc-4ae1065413b8", "metadata": {}, "source": [ "**3. Model building and Improvement:**" ] }, { "cell_type": "code", "execution_count": 64, "id": "bebe6512-44e6-42ea-a4e3-6c97a2e44521", "metadata": {}, "outputs": [], "source": [ "smt = SMOTE(random_state=42)\n", "X_train, y_train = smt.fit_resample(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 65, "id": "4edba1c4-f65e-4c7d-9682-238335c82cd5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 4130\n", "1 4130\n", "Name: Churn, dtype: int64" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train.value_counts()" ] }, { "cell_type": "markdown", "id": "82e753c0-8462-4738-8d56-9ff6f5abf31e", "metadata": {}, "source": [ "**A. Train a model using XGBoost. Also print best performing parameters along with train and test performance.**" ] }, { "cell_type": "code", "execution_count": 66, "id": "191bb9aa-24d6-4fd7-85a1-f8dda1bc0901", "metadata": {}, "outputs": [], "source": [ "clf_name = []\n", "roc_auc = []\n", "f1 = []\n", "def model_eval(clf, y_test, y_pred):\n", " print(classification_report(y_test, y_pred))\n", " cm=confusion_matrix(y_test, y_pred, labels=y_test.unique())\n", " disp = ConfusionMatrixDisplay(cm, display_labels=y_test.unique())\n", " disp.plot(cmap='cividis')\n", " m1 = roc_auc_score(y_test, y_pred)\n", " m2 = f1_score(y_test, y_pred)\n", " print('ROC_AUC_Score: {:.04f}'.format(m1))\n", " print('F1 Score: {:.04f}'.format(m2))\n", " clf_name.append(clf)\n", " roc_auc.append(m1)\n", " f1.append(m2)" ] }, { "cell_type": "code", "execution_count": 67, "id": "6429fafc-9520-4ea6-95f6-ee31bd0cf155", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[16:59:37] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", " precision recall f1-score support\n", "\n", " 0 0.90 0.67 0.77 1033\n", " 1 0.46 0.79 0.59 374\n", "\n", " accuracy 0.70 1407\n", " macro avg 0.68 0.73 0.68 1407\n", "weighted avg 0.78 0.70 0.72 1407\n", "\n", "ROC_AUC_Score: 0.7307\n", "F1 Score: 0.5856\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "xgb_clf = XGBClassifier(n_estimators=5, max_depth=1, max_leaves=2, random_state=42)\n", "xgb_clf.fit(X_train, y_train)\n", "y_pred = xgb_clf.predict(X_test)\n", "clf = 'XGBoost'\n", "model_eval(clf, y_test, y_pred)" ] }, { "cell_type": "markdown", "id": "286a1c81-e6f4-42f5-9e2b-4c3f67e3a2fc", "metadata": {}, "source": [ "**B. Improve performance of the XGBoost as much as possible. Also print best performing parameters along with train and test performance.**" ] }, { "cell_type": "code", "execution_count": 68, "id": "dfd9f34c-7027-4742-9152-bb7f05f2ea87", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[16:59:39] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", " precision recall f1-score support\n", "\n", " 0 0.89 0.76 0.82 1033\n", " 1 0.53 0.74 0.62 374\n", "\n", " accuracy 0.76 1407\n", " macro avg 0.71 0.75 0.72 1407\n", "weighted avg 0.80 0.76 0.77 1407\n", "\n", "ROC_AUC_Score: 0.7526\n", "F1 Score: 0.6192\n" ] }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "xgb_clf = XGBClassifier(n_estimators=500, max_depth=1, max_leaves=2, random_state=42)\n", "xgb_clf.fit(X_train, y_train)\n", "y_pred = xgb_clf.predict(X_test)\n", "clf = 'XGBoost'\n", "model_eval(clf, y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": null, "id": "9b785f79-66a4-4c91-8959-5fef125067f2", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "7ca99759-912a-49d3-9255-7140504f83b0", "metadata": {}, "source": [ "**Part-B**" ] }, { "cell_type": "markdown", "id": "d7906dec-3b50-4e8f-847c-3d88c9913f06", "metadata": {}, "source": [ "**DOMAIN:** *IT*\n", "\n", "**CONTEXT:** *The purpose is to build a machine learning workflow that will work autonomously irrespective of Data and users can save efforts\n", "involved in building workflows for each dataset.*\n", "\n", "\n", "**PROJECT OBJECTIVE:** *Build a machine learning workflow that will run autonomously with the csv file and return best performing model.*" ] }, { "cell_type": "markdown", "id": "64aaa5ad-6326-4e4b-a594-2c40dd62a904", "metadata": {}, "source": [ "**STEPS AND TASK:**" ] }, { "cell_type": "markdown", "id": "ccac3494-4b20-47a2-b628-681c509e164c", "metadata": {}, "source": [ "**1.** *Build a simple ML workflow which will accept a single ‘.csv’ file as input and return a trained base model that can be used for predictions. You can use\n", "1 Dataset from Part 1 (single/merged).*\n", "\n", "**2.** *Create separate functions for various purposes.*\n", "\n", "**3.** *Various base models should be trained to select the best performing model.*\n", "**4.** *Pickle file should be saved for the best performing model.*\n", "\n", "**Include best coding practices in the code:**\n", "\n", "*• Modularization*\n", "\n", "*• Maintainability*\n", "\n", "*• Well commented code etc.*" ] }, { "cell_type": "code", "execution_count": 69, "id": "f3678fd3-9c3e-4acd-82b2-b4b541ad2a9d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "[(1.0, 0.34509803921568627, 0.3176470588235294),\n", " (0.9529411764705882, 0.7568627450980392, 0.18823529411764706),\n", " (0.2549019607843137, 0.2901960784313726, 0.4196078431372549),\n", " (0.7058823529411765, 0.6039215686274509, 0.5215686274509804),\n", " (0.10980392156862745, 0.10588235294117647, 0.12549019607843137)]" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import plotly.express as px\n", "import plotly.graph_objects as go\n", "from plotly.subplots import make_subplots\n", "\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import StandardScaler\n", "from imblearn.over_sampling import SMOTE\n", "from sklearn.ensemble import BaggingClassifier\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier, GradientBoostingClassifier, VotingClassifier, StackingClassifier\n", "from xgboost import XGBClassifier, XGBRegressor\n", "from lightgbm import LGBMClassifier\n", "from catboost import CatBoostClassifier\n", "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, roc_auc_score, f1_score, ConfusionMatrixDisplay, r2_score\n", "\n", "np.random.seed(42)\n", "colors = ['#FF5851', '#F3C130', '#414A6B', '#B49A85', '#1C1B20']\n", "sns.color_palette(colors)" ] }, { "cell_type": "code", "execution_count": 70, "id": "378a573b-7e55-4985-9f83-390b01b40b50", "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('TelcomCustomer-Churn_1.csv')" ] }, { "cell_type": "code", "execution_count": 71, "id": "b2e57991-6b3e-485d-a2f3-b4f023672ac5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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customerIDgenderSeniorCitizenPartnerDependentstenurePhoneServiceMultipleLinesInternetServiceOnlineSecurity
07590-VHVEGFemale0YesNo1NoNo phone serviceDSLNo
15575-GNVDEMale0NoNo34YesNoDSLYes
23668-QPYBKMale0NoNo2YesNoDSLYes
37795-CFOCWMale0NoNo45NoNo phone serviceDSLYes
49237-HQITUFemale0NoNo2YesNoFiber opticNo
59305-CDSKCFemale0NoNo8YesYesFiber opticNo
61452-KIOVKMale0NoYes22YesYesFiber opticNo
76713-OKOMCFemale0NoNo10NoNo phone serviceDSLYes
87892-POOKPFemale0YesNo28YesYesFiber opticNo
96388-TABGUMale0NoYes62YesNoDSLYes
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" ], "text/plain": [ " customerID gender SeniorCitizen Partner Dependents tenure PhoneService \\\n", "0 7590-VHVEG Female 0 Yes No 1 No \n", "1 5575-GNVDE Male 0 No No 34 Yes \n", "2 3668-QPYBK Male 0 No No 2 Yes \n", "3 7795-CFOCW Male 0 No No 45 No \n", "4 9237-HQITU Female 0 No No 2 Yes \n", "5 9305-CDSKC Female 0 No No 8 Yes \n", "6 1452-KIOVK Male 0 No Yes 22 Yes \n", "7 6713-OKOMC Female 0 No No 10 No \n", "8 7892-POOKP Female 0 Yes No 28 Yes \n", "9 6388-TABGU Male 0 No Yes 62 Yes \n", "\n", " MultipleLines InternetService OnlineSecurity \n", "0 No phone service DSL No \n", "1 No DSL Yes \n", "2 No DSL Yes \n", "3 No phone service DSL Yes \n", "4 No Fiber optic No \n", "5 Yes Fiber optic No \n", "6 Yes Fiber optic No \n", "7 No phone service DSL Yes \n", "8 Yes Fiber optic No \n", "9 No DSL Yes " ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(10)" ] }, { "cell_type": "code", "execution_count": 72, "id": "358bbaa2-4834-4738-995c-f047b146caba", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(7043, 10)" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 73, "id": "2b6045d4-508b-4ebe-8fa7-021e983725e1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 7043 entries, 0 to 7042\n", "Data columns (total 10 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 customerID 7043 non-null object\n", " 1 gender 7043 non-null object\n", " 2 SeniorCitizen 7043 non-null int64 \n", " 3 Partner 7043 non-null object\n", " 4 Dependents 7043 non-null object\n", " 5 tenure 7043 non-null int64 \n", " 6 PhoneService 7043 non-null object\n", " 7 MultipleLines 7043 non-null object\n", " 8 InternetService 7043 non-null object\n", " 9 OnlineSecurity 7043 non-null object\n", "dtypes: int64(2), object(8)\n", "memory usage: 550.4+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 74, "id": "e58d507a-46d0-4673-bed3-5ab548631800", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "customerID 0\n", "gender 0\n", "SeniorCitizen 0\n", "Partner 0\n", "Dependents 0\n", "tenure 0\n", "PhoneService 0\n", "MultipleLines 0\n", "InternetService 0\n", "OnlineSecurity 0\n", "dtype: int64" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 75, "id": "159a493b-dd4b-4cb7-a545-0ae0afe93b82", "metadata": {}, "outputs": [], "source": [ "df.dropna(inplace=True)" ] }, { "cell_type": "code", "execution_count": 76, "id": "b82e06a0-fd44-42d3-b734-3767eba327a0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countmeanstdmin25%50%75%max
SeniorCitizen7032.00.1624000.3688440.000.00000.0000.00001.00
MonthlyCharges7032.064.79820830.08597418.2535.587570.35089.8625118.75
TotalCharges7032.02283.3004412266.77136218.80401.45001397.4753794.73758684.80
Churn7032.00.2657850.4417820.000.00000.0001.00001.00
gender_Female7032.00.4953070.5000140.000.00000.0001.00001.00
gender_Male7032.00.5046930.5000140.000.00001.0001.00001.00
Partner_No7032.00.5174910.4997290.000.00001.0001.00001.00
Partner_Yes7032.00.4825090.4997290.000.00000.0001.00001.00
Dependents_No7032.00.7015070.4576290.000.00001.0001.00001.00
Dependents_Yes7032.00.2984930.4576290.000.00000.0001.00001.00
PhoneService_No7032.00.0967010.2955710.000.00000.0000.00001.00
PhoneService_Yes7032.00.9032990.2955710.001.00001.0001.00001.00
MultipleLines_No7032.00.4813710.4996880.000.00000.0001.00001.00
MultipleLines_No phone service7032.00.0967010.2955710.000.00000.0000.00001.00
MultipleLines_Yes7032.00.4219280.4939020.000.00000.0001.00001.00
InternetService_DSL7032.00.3435720.4749340.000.00000.0001.00001.00
InternetService_Fiber optic7032.00.4402730.4964550.000.00000.0001.00001.00
InternetService_No7032.00.2161550.4116500.000.00000.0000.00001.00
OnlineSecurity_No7032.00.4972980.5000280.000.00000.0001.00001.00
OnlineSecurity_No internet service7032.00.2161550.4116500.000.00000.0000.00001.00
OnlineSecurity_Yes7032.00.2865470.4521800.000.00000.0001.00001.00
OnlineBackup_No7032.00.4389930.4963000.000.00000.0001.00001.00
OnlineBackup_No internet service7032.00.2161550.4116500.000.00000.0000.00001.00
OnlineBackup_Yes7032.00.3448520.4753540.000.00000.0001.00001.00
DeviceProtection_No7032.00.4399890.4964210.000.00000.0001.00001.00
DeviceProtection_No internet service7032.00.2161550.4116500.000.00000.0000.00001.00
DeviceProtection_Yes7032.00.3438570.4750280.000.00000.0001.00001.00
TechSupport_No7032.00.4937430.4999960.000.00000.0001.00001.00
TechSupport_No internet service7032.00.2161550.4116500.000.00000.0000.00001.00
TechSupport_Yes7032.00.2901020.4538420.000.00000.0001.00001.00
StreamingTV_No7032.00.3994600.4898220.000.00000.0001.00001.00
StreamingTV_No internet service7032.00.2161550.4116500.000.00000.0000.00001.00
StreamingTV_Yes7032.00.3843860.4864840.000.00000.0001.00001.00
StreamingMovies_No7032.00.3954780.4889880.000.00000.0001.00001.00
StreamingMovies_No internet service7032.00.2161550.4116500.000.00000.0000.00001.00
StreamingMovies_Yes7032.00.3883670.4874140.000.00000.0001.00001.00
Contract_Month-to-month7032.00.5510520.4974220.000.00001.0001.00001.00
Contract_One year7032.00.2093290.4068580.000.00000.0000.00001.00
Contract_Two year7032.00.2396190.4268810.000.00000.0000.00001.00
PaperlessBilling_No7032.00.4072810.4913630.000.00000.0001.00001.00
PaperlessBilling_Yes7032.00.5927190.4913630.000.00001.0001.00001.00
PaymentMethod_Bank transfer (automatic)7032.00.2192830.4137900.000.00000.0000.00001.00
PaymentMethod_Credit card (automatic)7032.00.2162970.4117480.000.00000.0000.00001.00
PaymentMethod_Electronic check7032.00.3363200.4724830.000.00000.0001.00001.00
PaymentMethod_Mailed check7032.00.2281000.4196370.000.00000.0000.00001.00
\n", "
" ], "text/plain": [ " count mean std \\\n", "SeniorCitizen 7032.0 0.162400 0.368844 \n", "MonthlyCharges 7032.0 64.798208 30.085974 \n", "TotalCharges 7032.0 2283.300441 2266.771362 \n", "Churn 7032.0 0.265785 0.441782 \n", "gender_Female 7032.0 0.495307 0.500014 \n", "gender_Male 7032.0 0.504693 0.500014 \n", "Partner_No 7032.0 0.517491 0.499729 \n", "Partner_Yes 7032.0 0.482509 0.499729 \n", "Dependents_No 7032.0 0.701507 0.457629 \n", "Dependents_Yes 7032.0 0.298493 0.457629 \n", "PhoneService_No 7032.0 0.096701 0.295571 \n", "PhoneService_Yes 7032.0 0.903299 0.295571 \n", "MultipleLines_No 7032.0 0.481371 0.499688 \n", "MultipleLines_No phone service 7032.0 0.096701 0.295571 \n", "MultipleLines_Yes 7032.0 0.421928 0.493902 \n", "InternetService_DSL 7032.0 0.343572 0.474934 \n", "InternetService_Fiber optic 7032.0 0.440273 0.496455 \n", "InternetService_No 7032.0 0.216155 0.411650 \n", "OnlineSecurity_No 7032.0 0.497298 0.500028 \n", "OnlineSecurity_No internet service 7032.0 0.216155 0.411650 \n", "OnlineSecurity_Yes 7032.0 0.286547 0.452180 \n", "OnlineBackup_No 7032.0 0.438993 0.496300 \n", "OnlineBackup_No internet service 7032.0 0.216155 0.411650 \n", "OnlineBackup_Yes 7032.0 0.344852 0.475354 \n", "DeviceProtection_No 7032.0 0.439989 0.496421 \n", "DeviceProtection_No internet service 7032.0 0.216155 0.411650 \n", "DeviceProtection_Yes 7032.0 0.343857 0.475028 \n", "TechSupport_No 7032.0 0.493743 0.499996 \n", "TechSupport_No internet service 7032.0 0.216155 0.411650 \n", "TechSupport_Yes 7032.0 0.290102 0.453842 \n", "StreamingTV_No 7032.0 0.399460 0.489822 \n", "StreamingTV_No internet service 7032.0 0.216155 0.411650 \n", "StreamingTV_Yes 7032.0 0.384386 0.486484 \n", "StreamingMovies_No 7032.0 0.395478 0.488988 \n", "StreamingMovies_No internet service 7032.0 0.216155 0.411650 \n", "StreamingMovies_Yes 7032.0 0.388367 0.487414 \n", "Contract_Month-to-month 7032.0 0.551052 0.497422 \n", "Contract_One year 7032.0 0.209329 0.406858 \n", "Contract_Two year 7032.0 0.239619 0.426881 \n", "PaperlessBilling_No 7032.0 0.407281 0.491363 \n", "PaperlessBilling_Yes 7032.0 0.592719 0.491363 \n", "PaymentMethod_Bank transfer (automatic) 7032.0 0.219283 0.413790 \n", "PaymentMethod_Credit card (automatic) 7032.0 0.216297 0.411748 \n", "PaymentMethod_Electronic check 7032.0 0.336320 0.472483 \n", "PaymentMethod_Mailed check 7032.0 0.228100 0.419637 \n", "\n", " min 25% 50% 75% \\\n", "SeniorCitizen 0.00 0.0000 0.000 0.0000 \n", "MonthlyCharges 18.25 35.5875 70.350 89.8625 \n", "TotalCharges 18.80 401.4500 1397.475 3794.7375 \n", "Churn 0.00 0.0000 0.000 1.0000 \n", "gender_Female 0.00 0.0000 0.000 1.0000 \n", "gender_Male 0.00 0.0000 1.000 1.0000 \n", "Partner_No 0.00 0.0000 1.000 1.0000 \n", "Partner_Yes 0.00 0.0000 0.000 1.0000 \n", "Dependents_No 0.00 0.0000 1.000 1.0000 \n", "Dependents_Yes 0.00 0.0000 0.000 1.0000 \n", "PhoneService_No 0.00 0.0000 0.000 0.0000 \n", "PhoneService_Yes 0.00 1.0000 1.000 1.0000 \n", "MultipleLines_No 0.00 0.0000 0.000 1.0000 \n", "MultipleLines_No phone service 0.00 0.0000 0.000 0.0000 \n", "MultipleLines_Yes 0.00 0.0000 0.000 1.0000 \n", "InternetService_DSL 0.00 0.0000 0.000 1.0000 \n", "InternetService_Fiber optic 0.00 0.0000 0.000 1.0000 \n", "InternetService_No 0.00 0.0000 0.000 0.0000 \n", "OnlineSecurity_No 0.00 0.0000 0.000 1.0000 \n", "OnlineSecurity_No internet service 0.00 0.0000 0.000 0.0000 \n", "OnlineSecurity_Yes 0.00 0.0000 0.000 1.0000 \n", "OnlineBackup_No 0.00 0.0000 0.000 1.0000 \n", "OnlineBackup_No internet service 0.00 0.0000 0.000 0.0000 \n", "OnlineBackup_Yes 0.00 0.0000 0.000 1.0000 \n", "DeviceProtection_No 0.00 0.0000 0.000 1.0000 \n", "DeviceProtection_No internet service 0.00 0.0000 0.000 0.0000 \n", "DeviceProtection_Yes 0.00 0.0000 0.000 1.0000 \n", "TechSupport_No 0.00 0.0000 0.000 1.0000 \n", "TechSupport_No internet service 0.00 0.0000 0.000 0.0000 \n", "TechSupport_Yes 0.00 0.0000 0.000 1.0000 \n", "StreamingTV_No 0.00 0.0000 0.000 1.0000 \n", "StreamingTV_No internet service 0.00 0.0000 0.000 0.0000 \n", "StreamingTV_Yes 0.00 0.0000 0.000 1.0000 \n", "StreamingMovies_No 0.00 0.0000 0.000 1.0000 \n", "StreamingMovies_No internet service 0.00 0.0000 0.000 0.0000 \n", "StreamingMovies_Yes 0.00 0.0000 0.000 1.0000 \n", "Contract_Month-to-month 0.00 0.0000 1.000 1.0000 \n", "Contract_One year 0.00 0.0000 0.000 0.0000 \n", "Contract_Two year 0.00 0.0000 0.000 0.0000 \n", "PaperlessBilling_No 0.00 0.0000 0.000 1.0000 \n", "PaperlessBilling_Yes 0.00 0.0000 1.000 1.0000 \n", "PaymentMethod_Bank transfer (automatic) 0.00 0.0000 0.000 0.0000 \n", "PaymentMethod_Credit card (automatic) 0.00 0.0000 0.000 0.0000 \n", "PaymentMethod_Electronic check 0.00 0.0000 0.000 1.0000 \n", "PaymentMethod_Mailed check 0.00 0.0000 0.000 0.0000 \n", "\n", " max \n", "SeniorCitizen 1.00 \n", "MonthlyCharges 118.75 \n", "TotalCharges 8684.80 \n", "Churn 1.00 \n", "gender_Female 1.00 \n", "gender_Male 1.00 \n", "Partner_No 1.00 \n", "Partner_Yes 1.00 \n", "Dependents_No 1.00 \n", "Dependents_Yes 1.00 \n", "PhoneService_No 1.00 \n", "PhoneService_Yes 1.00 \n", "MultipleLines_No 1.00 \n", "MultipleLines_No phone service 1.00 \n", "MultipleLines_Yes 1.00 \n", "InternetService_DSL 1.00 \n", "InternetService_Fiber optic 1.00 \n", "InternetService_No 1.00 \n", "OnlineSecurity_No 1.00 \n", "OnlineSecurity_No internet service 1.00 \n", "OnlineSecurity_Yes 1.00 \n", "OnlineBackup_No 1.00 \n", "OnlineBackup_No internet service 1.00 \n", "OnlineBackup_Yes 1.00 \n", "DeviceProtection_No 1.00 \n", "DeviceProtection_No internet service 1.00 \n", "DeviceProtection_Yes 1.00 \n", "TechSupport_No 1.00 \n", "TechSupport_No internet service 1.00 \n", "TechSupport_Yes 1.00 \n", "StreamingTV_No 1.00 \n", "StreamingTV_No internet service 1.00 \n", "StreamingTV_Yes 1.00 \n", "StreamingMovies_No 1.00 \n", "StreamingMovies_No internet service 1.00 \n", "StreamingMovies_Yes 1.00 \n", "Contract_Month-to-month 1.00 \n", "Contract_One year 1.00 \n", "Contract_Two year 1.00 \n", "PaperlessBilling_No 1.00 \n", "PaperlessBilling_Yes 1.00 \n", "PaymentMethod_Bank transfer (automatic) 1.00 \n", "PaymentMethod_Credit card (automatic) 1.00 \n", "PaymentMethod_Electronic check 1.00 \n", "PaymentMethod_Mailed check 1.00 " ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_df.describe().T" ] }, { "cell_type": "code", "execution_count": 77, "id": "e929d265-3cb1-45aa-831a-ea4b325aac6d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Numerical Columns: ['SeniorCitizen', 'tenure']\n", "Categorical Columns: ['customerID', 'gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity']\n", "\n", "\n", "\n", "Unique Values In Categorical columns:\n" ] }, { "data": { "text/plain": [ "[\"customerID: ['7590-VHVEG' '5575-GNVDE' '3668-QPYBK' ... '4801-JZAZL' '8361-LTMKD'\\n '3186-AJIEK']\",\n", " \"gender: ['Female' 'Male']\",\n", " \"Partner: ['Yes' 'No']\",\n", " \"Dependents: ['No' 'Yes']\",\n", " \"PhoneService: ['No' 'Yes']\",\n", " \"MultipleLines: ['No phone service' 'No' 'Yes']\",\n", " \"InternetService: ['DSL' 'Fiber optic' 'No']\",\n", " \"OnlineSecurity: ['No' 'Yes' 'No internet service']\"]" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "num_cols = df.select_dtypes(include='number')\n", "cat_cols = df.select_dtypes(include='object')\n", "print(f'Numerical Columns: {num_cols.columns.tolist()}')\n", "print(f'Categorical Columns: {cat_cols.columns.tolist()}\\n')\n", "print('\\n\\nUnique Values In Categorical columns:')\n", "[f'{col}: {cat_cols[col].unique()}' for col in cat_cols]" ] }, { "cell_type": "markdown", "id": "1e0ac563-d68f-47f7-80db-ce09c22c6997", "metadata": {}, "source": [ "**EDA**" ] }, { "cell_type": "code", "execution_count": 78, "id": "3f5387f7-4122-45fa-8cfc-dddb6b7ada71", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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7038Male0YesYes
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" ], "text/plain": [ " gender SeniorCitizen Partner Dependents\n", "0 Female 0 Yes No\n", "1 Male 0 No No\n", "2 Male 0 No No\n", "3 Male 0 No No\n", "4 Female 0 No No\n", "... ... ... ... ...\n", "7038 Male 0 Yes Yes\n", "7039 Female 0 Yes Yes\n", "7040 Female 0 Yes Yes\n", "7041 Male 1 Yes No\n", "7042 Male 0 No No\n", "\n", "[7043 rows x 4 columns]" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[['gender', 'SeniorCitizen','Partner','Dependents']]" ] }, { "cell_type": "code", "execution_count": 79, "id": "27c76933-4f80-4e76-a841-a16fef704160", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Male 50.48\n", "Female 49.52\n", "Name: gender, dtype: float64" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(cat_cols['gender'].value_counts()*100/cat_cols['gender'].shape[0]).round(2)" ] }, { "cell_type": "code", "execution_count": 80, "id": "bd3e33a0-8ddf-47cf-a611-83ed474a485d", 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" ], "text/plain": [ " gender Partner Dependents_No Dependents_Yes\n", "0 Female No 1655.0 145.0\n", "1 Female Yes 805.0 883.0\n", "2 Male No 1625.0 216.0\n", "3 Male Yes 848.0 866.0" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c = pd.get_dummies(data = cat_cols[['gender', 'Partner', 'Dependents']], columns=['Dependents']).groupby(['gender', 'Partner']).sum().reset_index()\n", "c" ] }, { "cell_type": "code", "execution_count": 82, "id": "d475ff51-d33f-4d56-9715-713c3f1d4539", "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertext": [ "Partner: No", "Partner: Yes", "Partner: No", "Partner: Yes" ], "marker": { "color": "#FF5851" }, "name": "No Dependents", "type": "bar", "x": [ "Female", "Female", "Male", "Male" ], "y": [ 1655, 805, 1625, 848 ] }, { "hovertext": [ "Partner: No", "Partner: Yes", "Partner: No", "Partner: Yes" ], "marker": { 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", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = go.Figure()\n", "fig.add_trace(go.Bar(\n", " x=c['gender'],\n", " y=c['Dependents_No'],\n", " name='No Dependents',\n", " hovertext=[\"Partner: No\", \"Partner: Yes\", \"Partner: No\", \"Partner: Yes\"],\n", " marker_color=colors[0]\n", "))\n", "fig.add_trace(go.Bar(\n", " x=c['gender'],\n", " y=c['Dependents_Yes'],\n", " name='Dependents',\n", " hovertext=[\"Partner: No\", \"Partner: Yes\", \"Partner: No\", \"Partner: Yes\"],\n", " marker_color=colors[2]\n", "))" ] }, { "cell_type": "code", "execution_count": 83, "id": "e34e52bb-ffa7-45a1-846a-144e49e67e27", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['customerID', 'gender', 'Partner', 'Dependents', 'PhoneService',\n", " 'MultipleLines', 'InternetService', 'OnlineSecurity'],\n", " dtype='object')" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cat_cols.columns" ] }, { "cell_type": "code", "execution_count": 84, "id": "b3f26088-b2f4-484a-8b05-aaa4d049751e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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07590-VHVEG0110011010010100100
15575-GNVDE03401101001100100010
23668-QPYBK0201101001100100010
37795-CFOCW04501101010010100010
49237-HQITU0210101001100010100
...............................................................
70386840-RESVB02401010101001100010
70392234-XADUH07210010101001010100
70404801-JZAZL01110010110010100010
70418361-LTMKD1401011001001010100
70423186-AJIEK06601101001100010010
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7043 rows × 20 columns

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" ], "text/plain": [ " customerID SeniorCitizen tenure gender_Female gender_Male \\\n", "0 7590-VHVEG 0 1 1 0 \n", "1 5575-GNVDE 0 34 0 1 \n", "2 3668-QPYBK 0 2 0 1 \n", "3 7795-CFOCW 0 45 0 1 \n", "4 9237-HQITU 0 2 1 0 \n", "... ... ... ... ... ... \n", "7038 6840-RESVB 0 24 0 1 \n", "7039 2234-XADUH 0 72 1 0 \n", "7040 4801-JZAZL 0 11 1 0 \n", "7041 8361-LTMKD 1 4 0 1 \n", "7042 3186-AJIEK 0 66 0 1 \n", "\n", " Partner_No Partner_Yes Dependents_No Dependents_Yes PhoneService_No \\\n", "0 0 1 1 0 1 \n", "1 1 0 1 0 0 \n", "2 1 0 1 0 0 \n", "3 1 0 1 0 1 \n", "4 1 0 1 0 0 \n", "... ... ... ... ... ... \n", "7038 0 1 0 1 0 \n", "7039 0 1 0 1 0 \n", "7040 0 1 0 1 1 \n", "7041 0 1 1 0 0 \n", "7042 1 0 1 0 0 \n", "\n", " PhoneService_Yes MultipleLines_No MultipleLines_No phone service \\\n", "0 0 0 1 \n", "1 1 1 0 \n", "2 1 1 0 \n", "3 0 0 1 \n", "4 1 1 0 \n", "... ... ... ... \n", "7038 1 0 0 \n", "7039 1 0 0 \n", "7040 0 0 1 \n", "7041 1 0 0 \n", "7042 1 1 0 \n", "\n", " MultipleLines_Yes InternetService_DSL InternetService_Fiber optic \\\n", "0 0 1 0 \n", "1 0 1 0 \n", "2 0 1 0 \n", "3 0 1 0 \n", "4 0 0 1 \n", "... ... ... ... \n", "7038 1 1 0 \n", "7039 1 0 1 \n", "7040 0 1 0 \n", "7041 1 0 1 \n", "7042 0 0 1 \n", "\n", " InternetService_No OnlineSecurity_0 OnlineSecurity_1 \\\n", "0 0 1 0 \n", "1 0 0 1 \n", "2 0 0 1 \n", "3 0 0 1 \n", "4 0 1 0 \n", "... ... ... ... \n", "7038 0 0 1 \n", "7039 0 1 0 \n", "7040 0 0 1 \n", "7041 0 1 0 \n", "7042 0 0 1 \n", "\n", " OnlineSecurity_No internet service \n", "0 0 \n", "1 0 \n", "2 0 \n", "3 0 \n", "4 0 \n", "... ... \n", "7038 0 \n", "7039 0 \n", "7040 0 \n", "7041 0 \n", "7042 0 \n", "\n", "[7043 rows x 20 columns]" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "do_dummy_cols = ['gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines',\n", " 'InternetService', 'OnlineSecurity']\n", "model_df = df.copy()\n", "model_df['OnlineSecurity'].replace(to_replace='Yes', value=1, inplace=True)\n", "model_df['OnlineSecurity'].replace(to_replace='No', value=0, inplace=True)\n", "model_df = pd.get_dummies(model_df, columns=do_dummy_cols)\n", "model_df" ] }, { "cell_type": "code", "execution_count": 85, "id": "e04f86c2-b106-4bab-bd21-e72485c2fcef", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countmeanstdmin25%50%75%max
SeniorCitizen7043.00.1621470.3686120.00.00.00.01.0
tenure7043.032.37114924.5594810.09.029.055.072.0
gender_Female7043.00.4952440.5000130.00.00.01.01.0
gender_Male7043.00.5047560.5000130.00.01.01.01.0
Partner_No7043.00.5169670.4997480.00.01.01.01.0
Partner_Yes7043.00.4830330.4997480.00.00.01.01.0
Dependents_No7043.00.7004120.4581100.00.01.01.01.0
Dependents_Yes7043.00.2995880.4581100.00.00.01.01.0
PhoneService_No7043.00.0968340.2957520.00.00.00.01.0
PhoneService_Yes7043.00.9031660.2957520.01.01.01.01.0
MultipleLines_No7043.00.4813290.4996870.00.00.01.01.0
MultipleLines_No phone service7043.00.0968340.2957520.00.00.00.01.0
MultipleLines_Yes7043.00.4218370.4938880.00.00.01.01.0
InternetService_DSL7043.00.3437460.4749910.00.00.01.01.0
InternetService_Fiber optic7043.00.4395850.4963720.00.00.01.01.0
InternetService_No7043.00.2166690.4120040.00.00.00.01.0
OnlineSecurity_07043.00.4966630.5000240.00.00.01.01.0
OnlineSecurity_17043.00.2866680.4522370.00.00.01.01.0
OnlineSecurity_No internet service7043.00.2166690.4120040.00.00.00.01.0
\n", "
" ], "text/plain": [ " count mean std min 25% \\\n", "SeniorCitizen 7043.0 0.162147 0.368612 0.0 0.0 \n", "tenure 7043.0 32.371149 24.559481 0.0 9.0 \n", "gender_Female 7043.0 0.495244 0.500013 0.0 0.0 \n", "gender_Male 7043.0 0.504756 0.500013 0.0 0.0 \n", "Partner_No 7043.0 0.516967 0.499748 0.0 0.0 \n", "Partner_Yes 7043.0 0.483033 0.499748 0.0 0.0 \n", "Dependents_No 7043.0 0.700412 0.458110 0.0 0.0 \n", "Dependents_Yes 7043.0 0.299588 0.458110 0.0 0.0 \n", "PhoneService_No 7043.0 0.096834 0.295752 0.0 0.0 \n", "PhoneService_Yes 7043.0 0.903166 0.295752 0.0 1.0 \n", "MultipleLines_No 7043.0 0.481329 0.499687 0.0 0.0 \n", "MultipleLines_No phone service 7043.0 0.096834 0.295752 0.0 0.0 \n", "MultipleLines_Yes 7043.0 0.421837 0.493888 0.0 0.0 \n", "InternetService_DSL 7043.0 0.343746 0.474991 0.0 0.0 \n", "InternetService_Fiber optic 7043.0 0.439585 0.496372 0.0 0.0 \n", "InternetService_No 7043.0 0.216669 0.412004 0.0 0.0 \n", "OnlineSecurity_0 7043.0 0.496663 0.500024 0.0 0.0 \n", "OnlineSecurity_1 7043.0 0.286668 0.452237 0.0 0.0 \n", "OnlineSecurity_No internet service 7043.0 0.216669 0.412004 0.0 0.0 \n", "\n", " 50% 75% max \n", "SeniorCitizen 0.0 0.0 1.0 \n", "tenure 29.0 55.0 72.0 \n", "gender_Female 0.0 1.0 1.0 \n", "gender_Male 1.0 1.0 1.0 \n", "Partner_No 1.0 1.0 1.0 \n", "Partner_Yes 0.0 1.0 1.0 \n", "Dependents_No 1.0 1.0 1.0 \n", "Dependents_Yes 0.0 1.0 1.0 \n", "PhoneService_No 0.0 0.0 1.0 \n", "PhoneService_Yes 1.0 1.0 1.0 \n", "MultipleLines_No 0.0 1.0 1.0 \n", "MultipleLines_No phone service 0.0 0.0 1.0 \n", "MultipleLines_Yes 0.0 1.0 1.0 \n", "InternetService_DSL 0.0 1.0 1.0 \n", "InternetService_Fiber optic 0.0 1.0 1.0 \n", "InternetService_No 0.0 0.0 1.0 \n", "OnlineSecurity_0 0.0 1.0 1.0 \n", "OnlineSecurity_1 0.0 1.0 1.0 \n", "OnlineSecurity_No internet service 0.0 0.0 1.0 " ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_df.describe().T" ] }, { "cell_type": "code", "execution_count": 86, "id": "c1dbf8b6-51de-4f75-bde9-ee7e403bd13e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.pairplot(model_df)" ] }, { "cell_type": "code", "execution_count": 89, "id": "b0292822-9add-4db3-973d-db5f6a7e527a", "metadata": {}, "outputs": [], "source": [ "LR_model=LogisticRegression()\n", "KNN_model=KNeighborsClassifier(n_neighbors=10)\n", "SVM_model=SVC()" ] }, { "cell_type": "code", "execution_count": 90, "id": "5683a117-44b0-46dd-a500-b93e48f79803", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LR: 0.798208 (0.010881)\n", "KNN: 0.783278 (0.011606)\n", "SVC: 0.734215 (0.012067)\n" ] }, { "data": { "image/png": "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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "models = []\n", "models.append(('LR', LR_model))\n", "models.append(('KNN', KNN_model))\n", "models.append(('SVC', SVM_model))\n", "\n", "# evaluate each model in turn\n", "results = []\n", "names = []\n", "scoring = 'accuracy'\n", "for name, model in models:\n", " kfold = model_selection.KFold(n_splits=10)\n", " cv_results = model_selection.cross_val_score(model, X,y, cv=kfold, scoring=scoring)\n", " results.append(cv_results)\n", " names.append(name)\n", " msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n", " print(msg)\n", "# boxplot algorithm comparison\n", "fig = plt.figure()\n", "fig.suptitle('Algorithm Comparison')\n", "ax = fig.add_subplot(111)\n", "plt.boxplot(results)\n", "ax.set_xticklabels(names)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 91, "id": "e39ed91c-453a-4337-9e35-62ad766dad3d", "metadata": {}, "outputs": [], "source": [ "LR_model=LogisticRegression()\n", "KNN_model=KNeighborsClassifier(n_neighbors=10)\n", "SVM_model=SVC()" ] }, { "cell_type": "code", "execution_count": 92, "id": "c85afaed-370f-4ae9-94a3-3b122eef69bf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LR: 0.799345 (0.012071)\n", "KNN: 0.784700 (0.009902)\n", "SVC: 0.734215 (0.000428)\n" ] }, { "data": { "image/png": "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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "results = []\n", "names = []\n", "scoring = 'accuracy'\n", "for name, model in models:\n", " kfold = model_selection.StratifiedKFold(n_splits=10)\n", " cv_results = model_selection.cross_val_score(model, X,y, cv=kfold, scoring=scoring)\n", " results.append(cv_results)\n", " names.append(name)\n", " msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n", " print(msg)\n", "# boxplot algorithm comparison\n", "fig = plt.figure()\n", "fig.suptitle('Algorithm Comparison')\n", "ax = fig.add_subplot(111)\n", "plt.boxplot(results)\n", "ax.set_xticklabels(names)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 93, "id": "278c5829-ef7c-4ca6-9cf0-8b9b39714e2a", "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=1)" ] }, { "cell_type": "code", "execution_count": 94, "id": "ff390540-40b1-453a-8b9f-7dd1c11f4861", "metadata": {}, "outputs": [], "source": [ "KNN = KNeighborsClassifier(n_neighbors= 5 , metric = 'euclidean' )" ] }, { "cell_type": "code", "execution_count": 95, "id": "f2e160d2-d20a-40d6-95d5-34619208ea7a", "metadata": {}, "outputs": [], "source": [ "KNN.fit(X_train, y_train)\n", "predicted_labels = KNN.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 96, "id": "d50c1e29-d47c-4dc9-b0e6-cdbf13e05cfe", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy on Training data: 0.8298666666666666\n", "Accuracy on Test data: 0.7690120824449183\n" ] } ], "source": [ "print('Accuracy on Training data:',KNN.score(X_train, y_train) )\n", "print('Accuracy on Test data:',KNN.score(X_test, y_test) )" ] }, { "cell_type": "code", "execution_count": 97, "id": "8828a581-7dd2-4dde-ad49-05d8fbcc232c", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "train_score=[]\n", "test_score=[]\n", "for k in range(1,51):\n", " KNN = KNeighborsClassifier(n_neighbors= k , metric = 'euclidean' ) \n", " KNN.fit(X_train, y_train)\n", " train_score.append(KNN.score(X_train, y_train))\n", " test_score.append(KNN.score(X_test, y_test))\n", "plt.plot(range(1,51),train_score)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 98, "id": "35739060-f36a-41e9-8ed7-ce7e75d344a2", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(range(1,51),test_score)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 99, "id": "b82b0ec2-519b-49fd-9782-b83fdb898823", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy on Training data for k 1 is 0.9976888888888888:\n", "Accuracy on Test data for k 1 is 0.7192608386638237:\n", "classification Matrix:\n", " precision recall f1-score support\n", "\n", " 0 0.81 0.80 0.81 1041\n", " 1 0.46 0.48 0.47 366\n", "\n", " accuracy 0.72 1407\n", " macro avg 0.64 0.64 0.64 1407\n", "weighted avg 0.72 0.72 0.72 1407\n", "\n", "Accuracy on Training data for k 3 is 0.8629333333333333:\n", "Accuracy on Test data for k 3 is 0.7683013503909026:\n", "classification Matrix:\n", " precision recall f1-score support\n", "\n", " 0 0.83 0.87 0.85 1041\n", " 1 0.56 0.49 0.52 366\n", "\n", " accuracy 0.77 1407\n", " macro avg 0.70 0.68 0.69 1407\n", "weighted avg 0.76 0.77 0.76 1407\n", "\n", "Accuracy on Training data for k 5 is 0.8298666666666666:\n", "Accuracy on Test data for k 5 is 0.7690120824449183:\n", "classification Matrix:\n", " precision recall f1-score support\n", "\n", " 0 0.82 0.88 0.85 1041\n", " 1 0.57 0.45 0.50 366\n", "\n", " accuracy 0.77 1407\n", " macro avg 0.70 0.66 0.68 1407\n", "weighted avg 0.76 0.77 0.76 1407\n", "\n", "Accuracy on Training data for k 7 is 0.8186666666666667:\n", "Accuracy on Test data for k 7 is 0.7746979388770433:\n", "classification Matrix:\n", " precision recall f1-score support\n", "\n", " 0 0.82 0.89 0.85 1041\n", " 1 0.59 0.44 0.50 366\n", "\n", " accuracy 0.77 1407\n", " macro avg 0.70 0.67 0.68 1407\n", "weighted avg 0.76 0.77 0.76 1407\n", "\n", "Accuracy on Training data for k 9 is 0.8184888888888889:\n", "Accuracy on Test data for k 9 is 0.7825159914712153:\n", "classification Matrix:\n", " precision recall f1-score support\n", "\n", " 0 0.82 0.90 0.86 1041\n", " 1 0.62 0.44 0.51 366\n", "\n", " accuracy 0.78 1407\n", " macro avg 0.72 0.67 0.69 1407\n", "weighted avg 0.77 0.78 0.77 1407\n", "\n", "Accuracy on Training data for k 11 is 0.8117333333333333:\n", "Accuracy on Test data for k 11 is 0.7825159914712153:\n", "classification Matrix:\n", " precision recall f1-score support\n", "\n", " 0 0.82 0.91 0.86 1041\n", " 1 0.62 0.42 0.50 366\n", "\n", " accuracy 0.78 1407\n", " macro avg 0.72 0.66 0.68 1407\n", "weighted avg 0.77 0.78 0.77 1407\n", "\n", "Accuracy on Training data for k 13 is 0.8065777777777777:\n", "Accuracy on Test data for k 13 is 0.7818052594171997:\n", "classification Matrix:\n", " precision recall f1-score support\n", "\n", " 0 0.82 0.91 0.86 1041\n", " 1 0.62 0.42 0.50 366\n", "\n", " accuracy 0.78 1407\n", " macro avg 0.72 0.66 0.68 1407\n", "weighted avg 0.77 0.78 0.77 1407\n", "\n", "Accuracy on Training data for k 15 is 0.8056888888888889:\n", "Accuracy on Test data for k 15 is 0.7853589196872779:\n", "classification Matrix:\n", " precision recall f1-score support\n", "\n", " 0 0.82 0.92 0.86 1041\n", " 1 0.63 0.41 0.50 366\n", "\n", " accuracy 0.79 1407\n", " macro avg 0.73 0.66 0.68 1407\n", "weighted avg 0.77 0.79 0.77 1407\n", "\n", "Accuracy on Training data for k 17 is 0.8028444444444445:\n", "Accuracy on Test data for k 17 is 0.7782515991471215:\n", "classification Matrix:\n", " precision recall f1-score support\n", "\n", " 0 0.81 0.92 0.86 1041\n", " 1 0.62 0.39 0.48 366\n", "\n", " accuracy 0.78 1407\n", " macro avg 0.71 0.65 0.67 1407\n", "weighted avg 0.76 0.78 0.76 1407\n", "\n", "Accuracy on Training data for k 19 is 0.8023111111111111:\n", "Accuracy on Test data for k 19 is 0.7746979388770433:\n", "classification Matrix:\n", " precision recall f1-score support\n", "\n", " 0 0.81 0.91 0.86 1041\n", " 1 0.61 0.38 0.47 366\n", "\n", " accuracy 0.77 1407\n", " macro avg 0.71 0.65 0.66 1407\n", "weighted avg 0.76 0.77 0.76 1407\n", "\n" ] } ], "source": [ "k=[1,3,5,7,9,11,13,15,17,19]\n", "for i in k:\n", " KNN = KNeighborsClassifier(n_neighbors=i, metric = 'euclidean' ) #Building knn with 5 neighbors\n", " KNN.fit(X_train, y_train)\n", " predicted_labels = KNN.predict(X_test)\n", " print('Accuracy on Training data for k {} is {}:'.format(i,KNN.score(X_train, y_train)))\n", " print('Accuracy on Test data for k {} is {}:'.format(i,KNN.score(X_test, y_test)))\n", " print(\"classification Matrix:\\n\",classification_report(y_test,predicted_labels))" ] }, { "cell_type": "code", "execution_count": 102, "id": "786ff5dd-6392-417e-a9d3-dad66492d263", "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)" ] }, { "cell_type": "code", "execution_count": 103, "id": "ef522945-3eb4-4665-8fac-d405dc9cc617", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 4130\n", "1 1495\n", "Name: Churn, dtype: int64" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train.value_counts()" ] }, { "cell_type": "code", "execution_count": 104, "id": "7e9ff889-60b5-4ac0-b839-7900ad60bcee", "metadata": {}, "outputs": [], "source": [ "sc = StandardScaler()\n", "X_train= sc.fit_transform(X_train)\n", "X_test= sc.transform(X_test)" ] }, { "cell_type": "code", "execution_count": 105, "id": "81477ba6-444e-4f17-bf11-2a6ba8192b03", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[-0.43931886, 0.98155578, 1.6599004 , ..., 1.90515869,\n", " -0.71617747, -0.5394682 ],\n", " [-0.43931886, -0.97154551, -0.56225219, ..., -0.52489066,\n", " 1.39630196, -0.5394682 ],\n", " [-0.43931886, 0.83706615, 1.75610395, ..., 1.90515869,\n", " -0.71617747, -0.5394682 ],\n", " ...,\n", " [-0.43931886, 0.92674937, 0.47350714, ..., 1.90515869,\n", " -0.71617747, -0.5394682 ],\n", " [-0.43931886, 0.02991714, -0.721544 , ..., -0.52489066,\n", " -0.71617747, 1.85367739],\n", " [ 2.27625101, 0.32221802, -0.9787973 , ..., -0.52489066,\n", " 1.39630196, -0.5394682 ]])" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train" ] }, { "cell_type": "code", "execution_count": 106, "id": "76e2c4a5-3c4a-462f-a79b-862b9dbbd484", "metadata": {}, "outputs": [], "source": [ "smt = SMOTE(random_state=42)\n", "X_train, y_train = smt.fit_resample(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 107, "id": "24946e35-d513-48dc-a57e-eed0249dfd21", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 4130\n", "1 4130\n", "Name: Churn, dtype: int64" ] }, "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train.value_counts()" ] }, { "cell_type": "code", "execution_count": 108, "id": "2fb23abc-c5c4-4a32-8abb-f90f00161fd0", "metadata": {}, "outputs": [], "source": [ "clf_name = []\n", "roc_auc = []\n", "f1 = []\n", "def model_eval(clf, y_test, y_pred):\n", " print(classification_report(y_test, y_pred))\n", " cm=confusion_matrix(y_test, y_pred, labels=y_test.unique())\n", " disp = ConfusionMatrixDisplay(cm, display_labels=y_test.unique())\n", " disp.plot(cmap='cividis')\n", " m1 = roc_auc_score(y_test, y_pred)\n", " m2 = f1_score(y_test, y_pred)\n", " print('ROC_AUC_Score: {:.04f}'.format(m1))\n", " print('F1 Score: {:.04f}'.format(m2))\n", " clf_name.append(clf)\n", " roc_auc.append(m1)\n", " f1.append(m2)" ] }, { "cell_type": "code", "execution_count": 109, "id": "65432078-91c8-4a06-9f02-a92a8942752d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "oob score: 0.8049636803874092\n", " precision recall f1-score support\n", "\n", " 0 0.90 0.77 0.83 1033\n", " 1 0.54 0.76 0.63 374\n", "\n", " accuracy 0.76 1407\n", " macro avg 0.72 0.76 0.73 1407\n", "weighted avg 0.80 0.76 0.78 1407\n", "\n", "ROC_AUC_Score: 0.7622\n", "F1 Score: 0.6310\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "bag_clf = BaggingClassifier(DecisionTreeClassifier(), n_estimators=500, max_samples=300, bootstrap=True,oob_score=True, n_jobs=-1, random_state=42)\n", "bag_clf.fit(X_train, y_train)\n", "y_pred = bag_clf.predict(X_test)\n", "print('oob score: ', bag_clf.oob_score_)\n", "clf = 'Bagging'\n", "model_eval(clf, y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": 110, "id": "28f017d3-fb50-4a88-8b17-ca8ea82f1762", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.90 0.76 0.83 1033\n", " 1 0.54 0.76 0.63 374\n", "\n", " accuracy 0.76 1407\n", " macro avg 0.72 0.76 0.73 1407\n", "weighted avg 0.80 0.76 0.77 1407\n", "\n", "ROC_AUC_Score: 0.7638\n", "F1 Score: 0.6320\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pas_clf = BaggingClassifier(DecisionTreeClassifier(), n_estimators=500, max_samples=300, bootstrap=False, n_jobs=-1, random_state=42)\n", "pas_clf.fit(X_train, y_train)\n", "y_pred = pas_clf.predict(X_test)\n", "clf = 'Pasting'\n", "model_eval(clf, y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": 111, "id": "1e26ecff-14f6-4f78-aad3-28050c647167", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.85 0.83 0.84 1033\n", " 1 0.57 0.61 0.59 374\n", "\n", " accuracy 0.77 1407\n", " macro avg 0.71 0.72 0.72 1407\n", "weighted avg 0.78 0.77 0.78 1407\n", "\n", "ROC_AUC_Score: 0.7207\n", "F1 Score: 0.5881\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "rs_clf = BaggingClassifier(DecisionTreeClassifier(), n_estimators=500, max_features=20, bootstrap_features=True, n_jobs=-1, random_state=42)\n", "rs_clf.fit(X_train, y_train)\n", "y_pred = rs_clf.predict(X_test)\n", "clf = 'Random Subspace'\n", "model_eval(clf, y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": 112, "id": "b0e706ed-f5ce-499c-9956-cf053dae2017", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.90 0.76 0.83 1033\n", " 1 0.54 0.76 0.63 374\n", "\n", " accuracy 0.76 1407\n", " macro avg 0.72 0.76 0.73 1407\n", "weighted avg 0.80 0.76 0.77 1407\n", "\n", "ROC_AUC_Score: 0.7638\n", "F1 Score: 0.6320\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "rp_clf = BaggingClassifier(DecisionTreeClassifier(), n_estimators=500, max_samples=500, max_features=10, bootstrap=True, bootstrap_features=False, n_jobs=-1, random_state=42)\n", "rp_clf.fit(X_train, y_train)\n", "y_pred = pas_clf.predict(X_test)\n", "clf = 'Random Patches'\n", "model_eval(clf, y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": 113, "id": "7bdf1517-0d11-499c-86f5-7ada4c7762e5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.89 0.78 0.83 1033\n", " 1 0.54 0.73 0.62 374\n", "\n", " accuracy 0.77 1407\n", " macro avg 0.72 0.75 0.73 1407\n", "weighted avg 0.80 0.77 0.77 1407\n", "\n", "ROC_AUC_Score: 0.7533\n", "F1 Score: 0.6224\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "rf_clf = RandomForestClassifier(n_estimators=500, max_samples=400, n_jobs=-1, random_state=42)\n", "rf_clf.fit(X_train, y_train)\n", "y_pred = rf_clf.predict(X_test)\n", "clf = 'Random Forest'\n", "model_eval(clf, y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": 114, "id": "13e9aed9-4f4c-472d-a5f7-f23c13ea4d99", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.89 0.75 0.81 1033\n", " 1 0.51 0.74 0.61 374\n", "\n", " accuracy 0.74 1407\n", " macro avg 0.70 0.74 0.71 1407\n", "weighted avg 0.79 0.74 0.76 1407\n", "\n", "ROC_AUC_Score: 0.7418\n", "F1 Score: 0.6051\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "etc_clf = ExtraTreesClassifier(n_estimators=1000, min_samples_split=300, random_state=42)\n", "etc_clf.fit(X_train, y_train)\n", "y_pred = etc_clf.predict(X_test)\n", "clf = 'Extra Trees'\n", "model_eval(clf, y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": 115, "id": "043ab2b9-d0db-4647-9c28-afd1cae2fd03", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.88 0.79 0.84 1033\n", " 1 0.55 0.71 0.62 374\n", "\n", " accuracy 0.77 1407\n", " macro avg 0.72 0.75 0.73 1407\n", "weighted avg 0.80 0.77 0.78 1407\n", "\n", "ROC_AUC_Score: 0.7529\n", "F1 Score: 0.6238\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ada_clf = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1), n_estimators=300, learning_rate=1.0)\n", "ada_clf.fit(X_train, y_train)\n", "y_pred = ada_clf.predict(X_test)\n", "clf = 'Adaboost'\n", "model_eval(clf, y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": 116, "id": "08bf533a-a0b6-49d2-8df7-58d9b37fdcfd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.89 0.82 0.85 1033\n", " 1 0.59 0.71 0.64 374\n", "\n", " accuracy 0.79 1407\n", " macro avg 0.74 0.76 0.75 1407\n", "weighted avg 0.81 0.79 0.80 1407\n", "\n", "ROC_AUC_Score: 0.7639\n", "F1 Score: 0.6423\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "gbdt_clf = GradientBoostingClassifier(n_estimators=500, learning_rate=1.0, max_depth=20, max_leaf_nodes=2, random_state=42)\n", "gbdt_clf.fit(X_train, y_train)\n", "y_pred = gbdt_clf.predict(X_test)\n", "clf = 'Gradient Boosting'\n", "model_eval(clf, y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": 117, "id": "19fe1daf-826e-4ff3-97d6-f88074901ae3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[17:21:55] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", " precision recall f1-score support\n", "\n", " 0 0.89 0.76 0.82 1033\n", " 1 0.53 0.74 0.62 374\n", "\n", " accuracy 0.76 1407\n", " macro avg 0.71 0.75 0.72 1407\n", "weighted avg 0.80 0.76 0.77 1407\n", "\n", "ROC_AUC_Score: 0.7526\n", "F1 Score: 0.6192\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "xgb_clf = XGBClassifier(n_estimators=500, max_depth=1, max_leaves=2, random_state=42)\n", "xgb_clf.fit(X_train, y_train)\n", "y_pred = xgb_clf.predict(X_test)\n", "clf = 'XGBoost'\n", "model_eval(clf, y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": 118, "id": "3e274e77-df08-4186-9c9c-e6cf344e99ac", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.88 0.82 0.85 1033\n", " 1 0.58 0.70 0.63 374\n", "\n", " accuracy 0.78 1407\n", " macro avg 0.73 0.76 0.74 1407\n", "weighted avg 0.80 0.78 0.79 1407\n", "\n", "ROC_AUC_Score: 0.7551\n", "F1 Score: 0.6303\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "lgbm_clf = LGBMClassifier(n_estimators=7000, max_depth=2, num_leaves=2, random_state=42)\n", "lgbm_clf.fit(X_train, y_train)\n", "y_pred = lgbm_clf.predict(X_test)\n", "clf = 'LightGBM'\n", "model_eval(clf, y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": 119, "id": "732d36d2-0420-4b09-8313-072573b05b96", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.86 0.83 0.85 1033\n", " 1 0.58 0.63 0.60 374\n", "\n", " accuracy 0.78 1407\n", " macro avg 0.72 0.73 0.72 1407\n", "weighted avg 0.79 0.78 0.78 1407\n", "\n", "ROC_AUC_Score: 0.7318\n", "F1 Score: 0.6028\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "cat_clf = CatBoostClassifier(n_estimators=500, max_depth=4, random_state=42, verbose=0)\n", "cat_clf.fit(X_train, y_train)\n", "y_pred = cat_clf.predict(X_test)\n", "clf = 'CatBoost'\n", "model_eval(clf, y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": 120, "id": "0e7cce1f-b5d9-4e8b-85ff-48861735af53", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[17:22:26] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", "[17:22:39] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", "XGBClassifier 0.7525870860532895\n", "XGBClassifier 0.6191536748329622\n", "LGBMClassifier 0.7551444057337798\n", "LGBMClassifier 0.6303030303030304\n", "CatBoostClassifier 0.731771332135776\n", "CatBoostClassifier 0.6028097062579821\n", "[17:22:53] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", "VotingClassifier 0.7521250601798406\n", "VotingClassifier 0.6251497005988024\n", " precision recall f1-score support\n", "\n", " 0 0.88 0.81 0.84 1033\n", " 1 0.57 0.70 0.63 374\n", "\n", " accuracy 0.78 1407\n", " macro avg 0.72 0.75 0.73 1407\n", "weighted avg 0.80 0.78 0.78 1407\n", "\n", "ROC_AUC_Score: 0.7521\n", "F1 Score: 0.6251\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "voting_clf = VotingClassifier(\n", "estimators=[('xgb', xgb_clf), ('lgbm', lgbm_clf), ('cat', cat_clf)],\n", "voting='hard')\n", "voting_clf.fit(X_train, y_train)\n", "y_pred = voting_clf.predict(X_test)\n", "for clf in (xgb_clf, lgbm_clf, cat_clf, voting_clf):\n", " clf.fit(X_train, y_train)\n", " y_pred = clf.predict(X_test)\n", " print(clf.__class__.__name__, roc_auc_score(y_test, y_pred))\n", " print(clf.__class__.__name__, f1_score(y_test, y_pred))\n", " \n", "clf = 'Voting Classifier'\n", "model_eval(clf, y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": 121, "id": "b3f8d4c0-8487-4d83-a904-75f589371229", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[17:23:53] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", "[17:26:42] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", "[17:26:43] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", "[17:26:45] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", "[17:26:46] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", "[17:26:48] WARNING: ..\\src\\learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", " precision recall f1-score support\n", "\n", " 0 0.88 0.81 0.84 1033\n", " 1 0.57 0.70 0.63 374\n", "\n", " accuracy 0.78 1407\n", " macro avg 0.72 0.75 0.73 1407\n", "weighted avg 0.80 0.78 0.78 1407\n", "\n", "ROC_AUC_Score: 0.7521\n", "F1 Score: 0.6251\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "stack_clf = StackingClassifier(estimators = [('bag', bag_clf),\n", " ('pas', pas_clf),\n", " ('rs', rs_clf),\n", " ('rp', rp_clf), \n", " ('rf', rf_clf),\n", " ('etc', etc_clf),\n", " ('ada', ada_clf),\n", " ('gbdt', gbdt_clf),\n", " ('xgb',xgb_clf),\n", " ('lgbm',lgbm_clf),\n", " ('cat',cat_clf)],\n", " final_estimator = rf_clf)\n", "\n", "stack_clf.fit(X_train, y_train)\n", "stack_clf.predict(X_test)\n", "clf = 'Stack'\n", "model_eval(clf, y_test, y_pred)" ] }, { "cell_type": "code", "execution_count": 122, "id": "2cfc792e-6332-42ef-9bcd-3d63a80b60de", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ROC_AUC_ScoreF1_Score
Classifier
Gradient Boosting0.7638800.642336
Pasting0.7637660.632044
Random Patches0.7637660.632044
Bagging0.7621760.630992
LightGBM0.7551440.630303
Voting Classifier0.7521250.625150
Stack0.7521250.625150
Adaboost0.7528860.623832
Random Forest0.7532780.622426
XGBoost0.7525870.619154
Extra Trees0.7418000.605061
CatBoost0.7317710.602810
Random Subspace0.7207070.588083
\n", "
" ], "text/plain": [ " ROC_AUC_Score F1_Score\n", "Classifier \n", "Gradient Boosting 0.763880 0.642336\n", "Pasting 0.763766 0.632044\n", "Random Patches 0.763766 0.632044\n", "Bagging 0.762176 0.630992\n", "LightGBM 0.755144 0.630303\n", "Voting Classifier 0.752125 0.625150\n", "Stack 0.752125 0.625150\n", "Adaboost 0.752886 0.623832\n", "Random Forest 0.753278 0.622426\n", "XGBoost 0.752587 0.619154\n", "Extra Trees 0.741800 0.605061\n", "CatBoost 0.731771 0.602810\n", "Random Subspace 0.720707 0.588083" ] }, "execution_count": 122, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = {'Classifier': clf_name, 'ROC_AUC_Score':roc_auc, 'F1_Score':f1}\n", "clf_report = pd.DataFrame(data=d).set_index('Classifier').sort_values('F1_Score', ascending=False)\n", "clf_report" ] }, { "cell_type": "code", "execution_count": 125, "id": "aa582f70-ce4e-4952-a8b5-34d215d909a3", "metadata": {}, "outputs": [], "source": [ "xgb_r = XGBRegressor(n_estimators=1000, random_state=42)\n", "xgb_r.fit(X_train,y_train)\n", "y_pred = xgb_r.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 126, "id": "1a467a32-3b92-4ab4-a4b8-29b3b70b23fb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "R Squared Score: -0.5021762989922274\n" ] } ], "source": [ "print(f'R Squared Score: {r2_score(y_pred, y_test)}')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 }